Managed File Transfer (MFT) Overview & Solution

If you familiar with middleware, you will be familiar with MFT / Managed File Transfer. It is one of the middleware category. If you know FTP (File Transfer Protocol), you might be know this MFT. I have worked with this MFT for about a year. I work with several partner that provide MFT solution or product. For Indonesian Market, MFT is a right solution to help company work and collaborate with their customers and partners. So what is MFT?

Wikipedia.com:
Managed file transfer ("MFT") refers to software or a service that manages the secure transfer of data from one computer to another through a network (e.g., the Internet). MFT software is marketed to corporate enterprises as an alternative to using ad-hoc file transfer solutions, such as FTP, HTTP and others.

Typically, MFT offers a higher level of security and control than FTP. Features include reporting (e.g., notification of successful file transfers), non-repudiation, auditability, global visibility, automation of file transfer-related activities and processes, end-to-end security, and performance metrics/monitoring. ensure secure, reliable and auditable data transfer to enhance various type of business process.

WhatIs.com

Managed file transfer (MFT) is a type of software used to provide secure internal, external and ad-hoc data transfers through a network. MFT products are built using the FTP network protocol. However, because federal regulations require that MFT products meet strict regulatory compliance standards, they include mechanisms to ensure a higher level of security and help keep information private.

MFT applications offer business automation, along with reporting and non-repudiation. An MFT solution should simplify management and ensure regulatory compliance while supporting all current security standards and methodology, including SSLencryption, X.509 encryption and proxy certificates.

Ipswitchft.com

MFT is a category of middleware software that ensures reliable, secure and auditable file transfer to enable critical business process. 

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My Definition: Managed File Transfer (MFT) is a middleware software that ensure secure, reliable and auditable data transfer to enhance various type of business process.

Problem we are facing nowadays related with MFT:

1. Low service level because of many disruption to the document exchanged and data transfer, such as unstable and narrowband network 
2. Low visibility because of lacking or minimal operational status
3. Not meet complication related data security best practices
4. High operational costs because too many complex manual processes, manual errors and exception drive up costs and lengthen time to value 
5. Continued growth in the amount of data, size of files and volumes of transactions  mandates highly scalable solutions to support expansion of business that demand simplicity and rapid response 

There are many solution related with this MFT. They offer several additional features to support the operational like Web Access, Mobile, etc... Some of them offer analytics functionality like dashboard too. Basically it is just an additional for the security, reliability and audibility data transfer. Just for your information too, some of the big brands also offer collaboration with another middleware like B2B (Business 2 Business) Integration. If MFT talk about file transfer, B2B talk about real time integration, and more automation. You have to decide based on your objective you want to achieve and of course your budget. Thank you for reading.

Optical Character Recognition - Overview

Wikipedia:
Optical character recognition (OCR) is the mechanical or electronic conversion of images of typed, handwritten or printed text into machine-encoded text. It is widely used as a form of data entry from printed paper data records, whether passport documents, invoices, bank statements, computerized receipts, business cards, mail, printouts of static-data, or any suitable documentation. It is a common method of digitizing printed texts so that it can be electronically edited, searched, stored more compactly, displayed on-line, and used in machine processes such as machine translation, text-to-speech, key data and text mining. OCR is a field of research in pattern recognition, artificial intelligence and computer vision.

Google:
Optical Character Recognition (OCR) lets you convert images with text into text documents using automated computer algorithms. Images can be processed individually (.jpg, .png, and .gif files) or in multi-page PDF documents (.pdf). These are some of the types of files suitable for OCR:

• Image or PDF files obtained using flatbed scanners
• Photos taken with digital cameras or mobile phones

Abbyy:
Optical Character Recognition, or OCR, is a technology that enables you to convert different types of documents, such as scanned paper documents, PDF files or images captured by a digital camera into editable and searchable data.

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Simple Document Imaging & Workflow Management
Imagine being able to “google” any document in your organization based on a keyword or number reference. With OCR recognition software, you are able to search for your scanned documents by any name or number reference.

3 Components of a Simple Document Management / Digital Archiving Solution

1. Scanning and Imaging: Documents are scanned into Searchable PDF format (Optical Character Recognition indexes all text, keyword and number references contained in the document).
2. Storage: Very simply and easily route documents into existing folders on a local network or create new folders directly from the scanning device / MFP.
3. Retrieval: Use Index search to locate any of your documents by name, keyword or number reference.

Key Benefits

• Security & Disaster Recovery: Easy backup & storage offsite.
• Productivity Gains: You are no longer manually digging through file cabinets when trying to locate a document.
• Accessibility: Documents can be viewed by many people within your office or from remote off-site locations.
• Organization: Documents don’t become lost since there is no need to re-file.
• Space Efficiency: Save office and cabinet space in your office / workplace.
• Cost Savings: Employee and company resources are free from the manual handling, filing and re-filing of paper documents.
• Environmental Sustainability: Documents are printed and copied at less of a frequency due to access by staff across the network.

Source : 4 Office Automation

Load Balancing & NLB - Overview

Load Balancing

Wikipedia:
In computing, load balancing distributes workloads across multiple computing resources, such as computers, a computer cluster, network links, central processing units or disk drives. Load balancing aims to optimize resource use, maximize throughput, minimize response time, and avoid overload of any single resource. Using multiple components with load balancing instead of a single component may increase reliability and availability through redundancy. Load balancing usually involves dedicated software or hardware, such as a multilayer switch or a Domain Name System server process.

Citrix:
Load balancing is a core networking solution responsible for distributing incoming traffic among servers hosting the same application content. By balancing application requests across multiple servers, a load balancer prevents any application server from becoming a single point of failure, thus improving overall application availability and responsiveness. For example, when one application server becomes unavailable, the load balancer simply directs all new application requests to other available servers in the pool.

Load balancers also improve server utilization and maximize availability. Load balancing is the most straightforward method of scaling out an application server infrastructure. As application demand increases, new servers can be easily added to the resource pool, and the load balancer will immediately begin sending traffic to the new server.

Network Load Balancing

Microsoft:
Network Load Balancing, a clustering technology included in the Microsoft Windows 2000 Advanced Server and Datacenter Server operating systems, enhances the scalability and availability of mission-critical, TCP/IP-based services, such as Web, Terminal Services, virtual private networking, and streaming media servers. This component runs within cluster hosts as part of the Windows 2000 operating system and requires no dedicated hardware support. To scale performance, Network Load Balancing distributes IP traffic across multiple cluster hosts. It also ensures high availability by detecting host failures and automatically redistributing traffic to the surviving hosts. Network Load Balancing provides remote controllability and supports rolling upgrades from the Windows NT 4.0 operating system.

The unique and fully distributed architecture of Network Load Balancing enables it to deliver very high performance and failover protection, especially in comparison with dispatcher-based load balancers. This white paper describes the key features of this technology and explores its internal architecture and performance characteristics in detail.


Webopedia:
Network Load Balancing (NLB) is a clustering technology offered by Microsoft as part of all Windows 2000 Server and Windows Server 2003 family operating systems. NLB uses a distributed algorithm to load balance network traffic across a number of hosts, helping to enhance the scalability and availability of mission critical, IP-based services, such as Web, Virtual Private Networking, streaming media, terminal services, proxy and so on. It also provides high availability by detecting host failures and automatically redistributing traffic to operational hosts.

Kumpulan Istilah Startup Teknologi - Oleh TechinAsia

Berbicara tentang startup adalah sesuatu yang sangat menarik apaagi dalam 3 tiga tahun belakangan ini. Banyak sekali startup baik diluar negeri maupun dalam negeri yang sering diperbincangkan secara "exclusive". Sebut saja mereka adalah Uber, AirBnB, GrabTaxi, Tokopedia, BukaLapak, OLX, UrbanIndo, Disdus, Lazada, Bridestory, Traveloka, Gojek dan seterusnya... Kalau saya tulis masih banyak lagi yang bisa saya tulis. Kiranya mereka mewakili beberapa industri yang ada.

Untuk yang di Indonesia sendiri beberapa lahir sebelum 2010, tapi mayoritas yang saya tulis diatas lahir setelah 2010 yang mana artinya umurnya masih < 5 tahun dan pendanaannya masih dalam tahap awal dan masih bisa sangat berkembang lagi. Selain daripada startup company itu sendiri, tentunya ada venture di belakangnya. Beberapa yang berasal dari Indonesia yang cukup terkenal adalah GEPI, Indigo Ideabox, dan MerahPutih.

Kembali ke topik artikel ini, dibawah ini adalah kumpulan istilah startup teknologi yang saya ambil dari TechinAsia Indonesia. Beberapa istilah sudah saya pahami dan beberapa juga baru saya mengerti disini. Tentunya ini akan menambah wawasan kita apalagi bagi kita yang berencana juga untuk mendirikan startup kita sendiri. Semoga bermanfaat.

Apabila Anda baru memasuki dunia startup teknologi dan sering menghadiri acara seminar, launching, atau membaca media online yang mengupas tentang startup teknologi mungkin Anda akan merasa bingung dengan sejumlah istilah asing seperti pivot, pra-series, acqui-hire, dan banyak istilah lainnya. Untuk membantu Anda memahami istilah-istilah tersebut, melalui artikel ini Tech in Asia akan memberikan penjelasan dari sejumlah istilah yang umum digunakan di dunia startup.

Investasi dan akuisisi

#Venture Capitalist: Investor yang berinvestasi pada sebuah venture capital, investasi penyertaan modal pada startup potensial.

#Venture Capitalist Utama (investor utama): merupakan venture capital yang bertanggung jawab untuk menyokong_ startup_ atau berinvestasi paling besar dalam pendanaan di tahap tertentu. Dalam kedua kasus, mereka terlibat aktif dalam perusahaan investasi. Mereka ikut dalam rapat dewan direksi sebagai direktur dan terlibat dalam portofolio sehari-hari.

#Venture Capital: merupakan perusahaan yang mendapat dan mengumpulkan pendanaan dari sejumlah konglomerat dan instisusi dengan tujuan untuk diberikan kepada perusahaan rintisan. Nantinya venture capital (VC) akan menerima imbalan sejumlah saham dari perusahaan rintisan yang mendapat pendanaan.

#Angel Investor: Seorang investor yang menggunakan dana pribadinya untuk berinvestasi pada sebuah startup dengan imbalan saham perusahaan tersebut.

#Round: adalah unit penggalangan dana. Round bisa disebut juga dengan tahap. Misalnya, round pertama disebut dengan “seed round” atau pendanaan tahap awal. Kemudian round berikutnya disebut “Seri A”, “Seri B”, “Seri C”, dan seterusnya. Round terakhir disebut sebagai “final round”.

#Seed funding: merupakan istilah bagi startup yang mendapat pendanaan tahap awal atau untuk kali pertama sebelum seri pendanaan berikutnya. Saat ini, besaran seed funding di Indonesia berkisar antara Rp500 juta hingga Rp1,5 miliar.

#Exit: memiliki dua pengertian, yakni exit yang baik dan buruk. Exit dikatakan baik apabila startup tersebut berhasil mencapai IPO atau M&A (Merge & Acquisition) dengan perusahaan lain. Sedangkan exit yang buruk adalah cara lain untuk mengatakan apabila sebuah startup gagal atau tutup karena sejumlah alasan.

#IPO (Initial Public Offering): Dalam bahasa Indonesia disebut juga penawaran publik perdana atau peluncuran ke pasar saham adalah momen saat saham sebuah perusahaan dijual kepada investor institusional yang kemudian dijual ke masyarakat umum di pasar saham untuk kali pertama.

#Acqui-hire: merupakan istilah yang dipopulerkan oleh Google pada pertengahan tahun 2000. Kondisi itu terjadi saat sebuah perusahaan besar merasa ide dari sebuah startup itu sangat buruk, tetapi memiliki tim yang berbakat. Sehingga perusahaan tersebut melakukan akuisisi untuk mendapatkan tim tersebut sebagai bonus.

Inkubator dan Akselerator

#Inkubator: adalah ruang pendukung, yang menjadi tempat berkembangnya startup. Kebanyakan inkubator menawarkan saran bagi para ahli, pelatihan serta dukungan keuangan untuk perusahaan muda, serta ruang kantor bagi perusahaan tersebut agar dapat berkembang.

#Akselerator: Program yang menerima aplikasi terbuka untuk mengikuti kelas gabungan startup (dikenal juga sebagai cohort ) yang terdiri atas sebuah tim pendiri kecil dengan ide yang dikembangkan secara eksternal. Program seperti ini menyediakan dukungan melalui sejumlah kecil modal awal, bimbingan, pelatihan, dan acara promosi dalam periode terbatas, biasanya 3–4 bulan. Startup yang lulus dari program ini akan berkesempatan mendapatkan investor saat demo day.

#Demo Day: Demo day biasanya diadakan pada tiap akhir masa inkubasi. Kegiatan ini merupakan kesempatan pertama bagi startup untuk bertemu dengan para calon investor.

#Coworking space: adalah ruang perkantoran tempat pekerja mandiri seperti entrepreneur , programmer lepas, dan desainer web saling berbagi. Co-working space akan menyediakan meja, ruang konferensi, dan koneksi internet untuk mendukung penggunanya bekerja. Tujuan utamanya bukan sekadar menyewakan ruang perkantoran, melainkan sebagai sebuah tempat komunitas yang sinergis tempat para entrepreneur penggunanya bisa mengembangkan jejaring mereka dan menghasilkan ide-ide baru.

Teknis

#API - Application Programming Interface: adalah interface dengan serangkaian fungsi atau rutinitas yang memungkinkan pengembangan untuk menyelesaikan tugas tertentu atau memungkinkan untuk berinteraksi dengan komponen software tertentu.

#UI (User Interface): atau desain antarmuka yang ditampilkan ke pengguna. Para pelaku di industri ini sering menaruh kedua akronim ini bersamaan ketika berbicara tentang fungsi dan estetika produk tertentu.

#UX (User Experience): mengacu pada pengalaman pengguna.

#Growth Hacking: merupakan istilah yang ditujukan bagi tim marketing, sales, atau asosiasi yang berhasil melakukan strategi khusus dengan melakukan “hacking” agar performa mereka meningkat drastis. Penambahan kata “hacking” ini dikarenakan mereka yang berprofesi di ranah nonteknis juga ingin disebut sebagai “hacker”.

#Pivot: merupakan istilah bagi sebuah startup yang melakukan perubahan saat ia sadar bahwa apa yang sudah dilakukan tidak berjalan. Akan tetapi perubahan yang dilakukan masih sesuai dengan ide awal mereka.

Bisnis

#Monetisasi: adalah konversi produk, atau aset, menjadi alat pembayaran yang sah. Pada dasarnya, ini adalah cara halus berbicara tentang bagaimana Anda dapat membuat sesuatu yang dapat dikomersilkan.

#KPI (Key Performance Indicator): cara mengukur seberapa efektif perusahaan Anda mencapai tujuannya. Banyak organisasi menggunakan ini untuk memastikan target terpenuhi. Hal ini sangat penting bagi perusahaan muda untuk melacak keberhasilan awal mereka di pasar.

#ROI (Return of Investment): yang tinggi memiliki arti bahwa suatu perusahaan menghasilkan uang di atas biaya investasi. Ini adalah singkatan favorit lainnya bagi para startup, digunakan untuk mengevaluasi keuntungan dibandingkan dengan modal yang diinvestasikan.


#Cost benefit analysis (CBA): atau analisis biaya manfaat adalah analisis tentang perbandingan atau selisih antara penerimaan yang diperoleh dan ongkos yang dikeluarkan dari suatu kegiatan. Jika penerimaan lebih besar daripada biaya yang dikeluarkan, kegiatan itu dianggap positif , artinya dapat dilakukan. Kalau tidak, kegiatan atau proyek tersebut tidak perlu dilaksanakan.


Sumber: TechinAsia Indonesia

6 Rules to Make Your Dashboards Stunningly Functional - by MicroStrategy

So far in this series, we’ve covered the basic principles that determine how we visually perceive information. Now it’s time to apply them to designing information-driven apps. Over years of working with dashboards and information presentation tools, I’ve identified 6 guiding principles that every information-driven app should strive to be:

1. Informative
2. Intuitive
3. Interactive
4. Stylish
5. Deductive
6. Actionable

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1: Informative
One of the most common design mistakes people make is forgetting the difference between ‘data’ and ‘information’. Simply providing access to data or displaying any available data on a subject does not make for a well-designed dashboard. Data needs to be presented in a format which transforms it into information that can be readily consumed. It is important to remember to design around the business process and not around data. Also, information in an application should flow in a way that reflects a business user’s train of thought.

Things that can make your dashboard informative:

• KPIs: Present up-to-date information on critical business metrics and put the most relevant information first.
• Trends: Show performance over time to add context.
• Comparisons: Show these based on time, geography, or other natural hierarchies relevant to the business process.
• Alerts or exceptions: Quickly communicate whether something is excellent, horribly wrong, below or above a threshold—anything that should spark an immediate action.
• Relative performance: Present information in relation to other business metrics/functions, internal competition-fostering metrics (ranks), and competitor information, if available.

2: Intuitive
If applications and reporting suites are difficult to use, users won’t want to use these tools, and critical information will go unused. There is nothing more frustrating to a business user than interfaces or workflows that disrupt the ability to get the job done. Rather, applications should be intuitive enough to drive user adoption while encouraging people to make decisions based on timely data.

Things that can make your information-driven app easy to use are:

• Keep it simple! Follow the basic guidelines of design discussed in the previous blogs of this series.
• Arrange the information in a way that makes sense to a business user. Use data subject areas, time granularity changes, ‘Cause and Effect’ relationships, etc. Group metrics or information that goes together to paint a story. Always provide a link to a FAQ page that explains the metric definitions.
• Avoid folder navigation. Use dashboard linking to create a workflow. Always provide a way back to the previous screen.
• Minimize the number of clicks to find answers.
• Make the app mirror a business workflow.

3: Interactive
People naturally want to interact with an application to explore information. They want to slice and dice, drill to, drill across, etc. In MicroStrategy, elements that provide interactivity include: selectors, panels, interactive features of a widget, layouts, info-windows, drill downs, navigation, and more. With so many choices to make an app interactive, it’s easier to point out what to avoid in order to keep your application interactive, intuitive, and easy to use.

Guidelines to keep it simple, yet interactive:

• Too much of anything is a bad idea! Focus on what’s important.
• Make everything navigable within 3 clicks.
• Don’t use more than 3 selectors. If you have more than 3 selectors, use the Filter panel so that the interface is not overcrowded with selection options.
• Only use info windows on certain elements. If you think that the user will always click/tap on an info window, then that information should be presented upfront.
• If there is a drill down enabled, the users should always have a way to get back to their previous screen.

4: Stylish
It’s all about form AND function today. Invoking one without the other can be a detriment to user adoption. To achieve this goal for dashboards and apps, I have always found it useful to work with a graphic designer. Remember to keep in mind the basic principles we have already discussed in the earlier blogs!

More tips to make your app look better and function great:

• Create a sense of pride and ownership by using corporate logos, corporate color palette, etc.
• Use updated icons, sleeker buttons, etc. Revisit older dashboards to update them for modern UI/UX looks.
• Use Icons in place of words. Let the Information be the most of the “wordy” content on the dashboard.
• Avoid using a lot of darker colors together; use contrast to increase text visibility.
• Use gradients of the same color instead of contrasting colors.

5&6: Deductive and Actionable
In the very first blog of this series, we established that visual dashboard design is successful if the user can identify a problem, determine why it occurred, and quickly decide what to do about it. So our dashboards should ultimately support this by being deductive and actionable. There are different ways to deliver “deductive” quality, depending on the user. Some users like to be presented with the final inference from data (high level executives, senior leadership, etc.) while other users, like functional analysts, may need access to a wider swath of data to be able to ask ad-hoc questions. Thus, you will have to adopt different tactics tailored for each audience. For analysts, consider integrating Visual Insight into the dashboard suite (more to come on this topic in the later blogs).

MicroStrategy Transaction Services provides the “actionable” function for a dashboard. Integrating in the ability to take action within the app makes it a true workflow-based app. A user can now see the data, figure out the next steps, and actually do it all in one place.

How to keep it simple:

• Design simple transactions.
• Preferably, keep the number of actions on a dashboard limited to avoid overload and confusion.
• Keep the transaction option very relevant to the problem at hand, and the information presented should allow them to complete the transaction

...

Source: Mamta Chirmade - MicroStrategy Blog

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I have been working with MicroStrategy in many opportunity. The differentiate or special things about MicroStrategy is no 6. MicroStrategy offer something that isn't owned by any vendor right now. It is transaction services. It allow user to do some action and create "simple" apps on the top of the Analytics, right beside their dashboard.

MicroStrategy offer very custom dashboard that can be built with your style and creativity. Although the development is not as simple as other vendors, the functionality and customization is very rich. There is also an offline capability where you can set up at the development so the users can enjoy the dashboard and transaction without being worry of the internet access. You can enjoy MicroStrategy on iOS and Android. MicroStrategy offer native mobile application, so you can access your data and information on the go.

What is Cohort Analysis? - Overview

What is Cohort Analysis?

Wikipedia.com

Cohort analysis is a subset of behavioral analytics that takes the data from a given eCommerce platform, web application, or online game and rather than looking at all users as one unit, it breaks them into related groups for analysis. These related groups, or cohorts, usually share common characteristics or experiences within a defined timespan. Cohort analysis allows a company to “see patterns clearly across the lifecycle of a customer (or user), rather than slicing across all customers blindly without accounting for the natural cycle that a customer undergoes.”

CohortAnalysis.com

A cohort is a group of people who share a common characteristic over a certain period of time.

Yoast.com

A cohort is any group of people sharing a characteristic. 

...

Example from CohortAnalysis.com

Perhaps the most popular cohort analysis is one that groups customers based on their "join date," or the date when they made their first purchase. Studying the spending trends of cohorts from different periods in time can indicate if the quality of the average customer being acquired is increasing or decreasing in over time.

Cohort Analysis in Google Analytics by Yoast

So a cohort analysis is basically the analysis of a group of people, in this case people who interacted with your website at the same date or date range. When clicking Cohort Analysis in Google Analytics, it’ll look something like this:

I don’t know about you, but this isn’t really immediately clear to me, so let me walk you through how to look at it. The chart at the top is a visualization of the average user retention (percentage of returning visitors) for the date range, which is 7 days by default.

The most interesting, however, is the table below the chart. This actually gives us insight in what percentage of people returned to your site within 7 days of visiting it for the first time. Day 0 corresponds with the date in the first column. Day 1 is the first day after someone visited your website for the first time. So the 4.32% at Day 1 in the March 10th row means that 4.32% of the people who visited yoast.com for the first time on March 10th, visited yoast.com again on the next day (March 11th). Day 2 is the second day (March 12th) and so on.

Note: this is a breakdown of New Users, so although it says “All Sessions”, this only includes people having visited your site for the first time.

#What can I do with this?

This is a question that I immediately asked myself. It wasn’t completely clear to me right away, so I might be a bit slow, or it’s just not that obvious. I’ll let you be the judge of that ;)

Let me give you an example (not yoast.com, by the way):

So what happened on March 14th or 15th that made people who visited this website for the first time on March 14th visit again the next day? The retention rate is about 2% higher there, and even on day 2 the retention rate is higher. Maybe they wrote a nice post? This can be a great way of figuring out whether what you’re trying (new content, new campaigns, etc.) is actually working.

Breaking down the cohort

If you need a more specific look on what’s happening, either because you don’t know why the retention rate was lower/higher, or because you’re just a data geek, you’re in luck. You can actually ‘break down’ your cohort analysis by using segments. For instance, if I were to use the Mobile and Tablet Traffic segment on the data above:

Google Analytics will give me this cohort report:

This shows the data for people who not only visited your website for the first time in the set timeframe, but were also on a smartphone or tablet when viewing the site. You can have up to 4 of such segments active at the same time. This way you can see whether the (expected) effect happened for all sorts of people, such as people on mobile phones, people from search engines or direct visitors, etc.

Other metrics

You can actually select quite a few metrics that will make the cohort analysis useful for a lot more than returning visitors:

Although the Cohort Type has a dropdown, it actually just has the one option. The Cohort Size can be set to ‘by day’, ‘by week’ or ‘by month’ and the Date Range will change accordingly. The most interesting though, is the Metric dropdown. You can select a lot of per user metrics (revenue, pageviews, transactions, etc.) or total metrics (again revenue, pageviews, etc.) apart from the Retention metric I used in the examples above.

This means you can actually see a lot of effects, such as whether your overall revenue or revenue per user has increased after a post or campaign. Of course, you can normally see your sales or revenue increase if you have a successful campaign, but this data is different.

You can now see how much revenue you got from people that visited your website for the first time on a specific date and see if these new visitors bought something on that date or in the days to follow. And since you can see this for an entire date range, you’ll also be able to see if that’s a higher or lower revenue than was to be expected.

Let me give you an example. Say you changed your landing page recently, which is tailored to just convincing new visitors of your site to buy a product. You could just be looking at the revenue from new visitors and see if it increases. However, if a visitor were to visit your website for the first time, only to return the next day to buy your product, Google Analytics wouldn’t show it as a new visitor anymore. And that’s why these cohorts actually work: the visitor was new at the set date, so even if they buy the product a day (or 2, or more) later, they’ll still show up in the cohort analysis. So you’re not just measuring direct effect anymore, you’re measuring delayed effects as well!

By the way, to be sure you have just the visitors that visited that specific landing page, you should create a segment for visitors who visited that page.

#The downsides

While looking at the cohort analysis for yoast.com, I noticed that the Retention metric is quite difficult for our domain. Our traffic, even from the new visitors, is just too stable. The pattern was just the same all the time, no matter what date range I selected. This is probably because we have such a steady flow of new visitors, mainly from Google, that any lift here would only be a small change in percentage.

So, the changes in the percentages are too small; if everything between 3.5% and 4.5% is the same color, it’s pretty hard to distinguish any real differences. Of course, I could just look at the percentages, but that’s just not as convenient.

More importantly, though, we can only create cohorts based on Acquisition Date at the moment, which is a nice start, but I do really hope they’ll start adding more Cohort Types. Just the Acquisition Date is really not enough, for me at least. I’d love to see cohorts of people buying a specific product (category), for instance.

#Summing up

The cohort analysis can definitely give you some insights that weren’t readily available before. However, it does still require more than just basic knowledge of Google Analytics and might be a little confusing in the beginning. So I’m not completely sold on this feature yet, but to be fair; it is still in beta, so who knows how much better it will get right?

Source : Yoast

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Other Useful Links : Use this spreadsheet for churn, MRR, and cohort analysis

Source : AndrewChen.co

My BI Project Experience & Top Six Worst Practices in Business Intelligence

Today, let's talk about BI Project, especially about why the project failed. In fact, BI projects fail at an astonishingly high rate – between 70 percent and 80 percent, according to Gartner. It is very interesting because I also have experienced "not good" BI project. Many factors that happened at that time. One of biggest factor I hate very much is "politic". My case may be an extra practice from the Article I would like to share here by Information Builders. 

In my case you should have strong sponsor that can support your team. BI project involve so many stakeholders if the scope of work is enterprise wide. You gonna face people across division and departments. Many department / division means many boss that has their own purpose and ego. You need to have big big boss in your side to ensure the boss under the big big boss support you. What am I talking about? I talk about data privacy and ownership. Sometimes even in one company, the unit under them is not synergy and not trust each other. What a bad teamwork?!

Okay now let's speak about the main topic of this article : Top Six Worst Practices in Business Intelligence. Let's start 1 by 1 :

Worst Practice 1: Buying What Analysts Want Without Considering Other Users

Most companies make business intelligence (BI) purchasing decisions based on input from
just one group of users – the business analysts. This single perspective, however, creates many problems.

Analysts select tools that they are already familiar with, or ones that are similar to those they have used in past. This severely limits broad BI adoption across an organization, and minimizes ROI. Because analyst-chosen tools are heavily biased towards their own skills and needs, they are way too complex for the average business user. The majority of users within an organization require extensive assistance from either the analysts themselves, or IT staff, in order to access and interact with enterprise information.

Perhaps this is why, since 2009, BI usage rates have remained flat at around 25 percent. BI Scorecard’s Cindi Howson says that close to 80 percent of employees who make decisions still “lack the tools to make them on facts, relying instead on static spreadmarts or gut feel.” 

Ease of use is a critical success factor for BI, but what is intuitive for one type of user may be complicated for another. Tools are for professionals, not for typical information consumers. Business users don’t need tools; they need different, more intuitive approaches to analysis. Companies that implement only on a handful of core tools that offer many powerful analytical functions through sophisticated interfaces – the tools that the analysts want – will alienate a large portion of their BI audience.

The Solution
Empowering different types of information consumers with different analytical tools is the key to BI pervasiveness, and ultimately BI success. Extending BI and analytics to all users –particularly frontline and operational employees, customers, and business partners – promotes better decision-making enterprise-wide and aligns operations to strategic and growth goals.

The most successful BI strategies take all information needs into account, and ensure that
supporting solutions satisfy the requirements of not only the analysts and power users, but also many different kinds of information consumers at the strategic, tactical, and operational levels. The selection committee should include business users as well as analysts to ensure that all users can embrace the chosen solution, regardless of their skill set or technical savvy.

My Opinion
I think it depends on your project stakeholder. Is it enterprise wide or only 1 department? By the way, is it align to the IT strategy? Some products are departmental, some products are enterprise ready. Sometimes when we talk about the "future" plan, some customer will prefer buy enterprise ready product, but of course it will be "overkill" until the time is come. Otherwise they will choose the departmental, and when in the future the customer need to more, or other department want to use / leverage the BI, it will become problem too. In my perspective we should choose a product that can support small or big. Simple.

Worst Practice 2: Buying New Data Discovery Tools Without Changing the Excel Mindset

Many companies rely on Microsoft Excel to facilitate the analysis and sharing of vital business information. The problems with this approach are well known: multiple versions of the truth caused by limited version control, lack of auditing ability, and high error rates and data quality problems. These issues negatively impact planning and decision-making, and the damage increases as disparate and conflicting spreadsheets circulate throughout an organization.

Few realize, however, that the slick new data discovery tools they’ve implemented to supplement those spreadsheets are nothing more than prettier versions of Excel. These products may enhance the visual appeal of the information being analyzed, but they still create the risk of inconsistent insight and flawed decisions. Like Excel, they lack version control and auditing ability, and have no way to ensure data integrity. Users have their own data sets and their own means of manipulating information, so they’ll arrive at different conclusions even though they use the same tools.

The most successful BI environments keep people connected and ensure a single, consistent view of enterprise information – even if they use different features and capabilities to perform their analyses.

The Solution
Companies need to move away from the Excel mindset, and implement a broad-reaching BI platform with a wide array of functionality. This eliminates the drawbacks of Excel and data discovery tools, while letting users conduct analyses and manipulate data the way they want to.

Whether they want spreadsheets, sophisticated visualizations, or pre-built BI apps that provide direct answers to specific business questions, a comprehensive BI platform will satisfy everyone’s needs, while preserving integrity, consistency, and auditing ability.

My Opinion
This is a fixed price. Excel is very good but they are not the best. You cannot change / replace it completely but you have to try what is impossible. The security and inconsistency are the biggest risks when we still keep using excel. This can be a good reason but don't forget to provide the replacement. Export feature is a must to address the need of excel.

Worst Practice 3: Making a BI Purchasing Decision Based on One Hot Feature

Companies often purchase a BI tool as a knee-jerk reaction to very specific, very narrow demands. A functional user insists on a certain feature to make her job a bit easier. A business manager wants a new analytical capability to solve a specific problem. An executive reads about new functionality and pushes for its purchase simply because it’s “cool”. These are typical scenarios, where one user or user group hijacks the entire evaluation process.

This tunnel vision is harmful to any business intelligence strategy. When BI efforts focus on just one requirement, broader-reaching analysis needs or future requirements are left out of the planning and solution-selection process. As needs change and grow, or as new requirements emerge, organizations struggle to evolve and expand the BI environment accordingly. They’ll purchase a series of disparate tools to address one need at a time, creating BI silos throughout the enterprise and driving up total cost of ownership. They’ll also frequently ignore the back-end information infrastructure to deliver the needed feature as quickly as possible, leading to a long-term maintenance nightmare.

Additionally, once they learn more about the selected tool, users who focused on a single feature in the first place often find the tool insufficient to address their higher-level needs. They go back to IT with demands for new tools, and the process starts all over again.

The Solution
According to Wayne Eckerson, director of BI Leadership Research, some tools “satisfy the parochial needs of individual workgroups or departments, while BI suites provide an integrated experience and architecture that addresses the entire spectrum of BI needs in an organization and is thus easier to administer.”

The most effective and economical approach is to choose a flexible and extensible platform with a broad range of capabilities. Companies can deploy the most urgent features and functions right away – the dashboard that users say they can’t live without, or the customer-facing BI environment that will keep clients from defecting to a competitor – and then easily add predictive analytics, data visualization, enterprise search, or other advanced capabilities as new needs arise.

Even if the BI platform lacks the hot, new feature, the vendor will most likely add it soon. On the other hand, the vendor of the “popular” feature won’t be able to quickly catch up on all the other capabilities that are already missing from their tool.

The platform approach addresses immediate requirements, while future-proofing the BI strategy. It also avoids the selection bias highlighted in the previous worst practice, by meeting the needs of all present and future stakeholders – not just those who make the most noise to get what they want.

My Opinion
Buy a BI product that can satisfy all stakeholder and also has a clear vision and if necessary have a clear roadmap for their product. To make sure of it, you can see it from how fast the company release the new patch or upgrade. How fast the company follow the market or become the market leader. You can see the commitment on it because most of the product will force you to buy ATS / renewal. It is not cheap.

Worst Practice 4: Lack of a Concrete Data Quality Strategy

Most organizations want to give their analysts new business intelligence tools as quickly as possible. Yet, in their rush to rapidly implement and roll out bigger and better BI capabilities, they fail to consider the integrity of the information sources with which those analysts work. They either overlook data quality needs completely, or address potential data quality problems at a later time.

This oversight creates monumental problems. Sound business decisions depend upon optimum data accuracy, consistency, timeliness, and completeness. Data integrity is even more important when you deploy advanced analytics. One bad record can dramatically change a conclusion, forecast, or estimate.

Most companies don’t embark on a data quality initiative until after things have gone horribly wrong. A BI solution will only succeed if the underlying data can be trusted. Lack of a solid data quality management plan as part of a BI initiative will lead to poor results, and may actually do more harm than good.

Advanced analytics and BI tools are quite reliant on the “strong fundamentals of data capture, cleansing and governance.” According to a recently published Aberdeen research brief, best-inclass companies are three times more likely to adopt data quality tools, a decision that directly with “increased performance in data analysis, employee efficiency, and the speed and accuracy of business decisions.”

The Solution
Many companies correct their data integrity problems by cleansing data as it is loaded into a data warehouse, data mart, or other repository, but this approach won’t tackle quality problems at their source. Corrected data is never reconciled with back-end systems, which means quality problems will still exist in real-time operational analytical scenarios.

A comprehensive data quality management solution, embedded directly into the BI environment, will ensure optimum integrity across all enterprise information assets. 

My Opinion
Data quality is a must. Who want to view the wrong data and show wrong information. No one! Some BI tools already provide the tool to do ETL things. I think the real process should be conducted before the data enter the data warehouse. So it is a bit late unless you need to analyze the data not from "system", just like excel.

Worst Practice 5: Not Considering Mobile Users in Your BI Strategy

Mobile First is an emerging development practice that eliminates the problems and issues
that occur when IT teams slap a mobile interface onto sophisticated, graphics-heavy websites that were originally developed for desktop formats. In light of the growth trend in mobile consumption – people are now using smartphones more often than laptops – companies must consider the Mobile First approach for their BI applications. If they fail to address the needs of mobile BI consumers, they’ll experience low levels of BI adoption, and ultimately, diminished returns on BI investment.

The needs of mobile users cannot be an afterthought. They must be addressed as plans are being laid out. For example, organizations must take into account smaller screen sizes, bandwidth and connectivity constraints, and consumer-style expectations for ease of use and an engaging and interactive experience. Multiple devices must also be supported to drive BI pervasiveness. Users must be able to access BI content via their smartphone or tablet of choice. A BI solution that forces organizations to build native apps for each type of device in use will drain time, resources, and money.

The Solution
Don’t ignore the needs of mobile users, or design a BI application for PC users and simply adjust it for mobile access. Organizations must ensure that all BI content is mobile-optimized. They must meet the demands of mobile BI consumers first, and then expand that mobile content to be incorporated into portals, dashboards, and other PC-based BI environments.

The key is to determine which mobile apps will have the greatest impact or generate the most revenue, and develop those first. With the right BI platform in place – one that is truly device agnostic – organizations can even let users choose which content they want to see on their mobile device, effectively using “crowd-sourcing” to determine priorities.

My Opinion
Yes, the mobile revolution is now! No excuse for this. For Indonesian market you need to make sure that the application is support Android. The management are also using Android, not only IOS. Maybe in the future it will changed because I believe Microsoft is on the way to make this change by it's Windows 10.

Worst Practice 6: Ignoring New Data, New Sources, and New Compliance Requirements

Today’s businesses operate in a new world order, one that involves rapidly growing volumes of data generated during increasingly complex transactions. Big data must be properly harnessed to drive business performance and ensure adherence to constantly changing regulatory guidelines.

New sources of information are also coming to light. Social media sites, blogs, e-mail messages, and other communication vehicles contain a wealth of vital, real-time business insight that can’t be obtained through surveys, focus groups, and other traditional forms of sentiment collection and opinion gathering. Companies can stay one step ahead by finding new ways to tap into these types of unstructured data and leverage it for competitive advantage.

But few BI platforms easily adapt to emerging requirements like these. For example, many BI solutions don’t scale, or require huge proprietary hardware appliances to support analysis against large data volumes. Some lack the ability to incorporate unstructured information into the environment or can’t retrieve data from important new sources like Facebook, blogs, and Twitter. And others can’t reconcile information across diverse infrastructures and big data environments to create a single, consistent view of key business information.

Partnering with the wrong vendor will hinder insight for decision-making purposes, and provide an incomplete picture of the state of the business. Organizations that choose a BI platform that can access all the information available to them – no matter how much of it there is, or where it comes from – will achieve true BI success.

The Solution
Social media is the hot new data source. Organizations must seamlessly collect this data, and reconcile it with other enterprise assets. Furthermore, columnar databases and storage facilities for big data, like Hadoop, didn’t exist five years ago but are critical now. A BI vendor must not only provide these capabilities, but also ensure they are fully integrated into the environment in a cohesive way.

My Opinion
As the company grow, the data inside it will grow to. New data means new insight. Company need to leverage those things in order to keep on the right track. Similar to that case, BI need to be supported by data and new data. BI product need to grow and support new trend and technology.

Conclusion

Some of the worst practices mentioned in this paper may seem like common sense. However, high BI failure rates demonstrate that these worst practices are, indeed, put into effect more frequently than you might think. When trade journalists, vendors, and industry consultants are constantly promoting the “latest and greatest” technology and all its benefits, it’s easy to get caught up in the hype.

But now that you are aware of these six worst practices, you can prevent them from standing in the way of BI success in your organization. You’ll make the right choices, with the right goals in mind, to lay the groundwork for widespread user adoption and rapid, measurable return from your business intelligence investment.

Source 
Top Six Worst Practices in Business Intelligence

Top 10 Business Questions That Drive Your BI Technology Requirements

This original article is taken from Birst in 2014. I will summary it here and add my opinion related to the article. I think this article is very powerful and still valid in 2015 and in upcoming years, because still many company has not own any BI yet especially in Indonesia, even some of the still think it is not important, it is too expensive or it is not mandatory. So let's straight to the point :

Question 1: Do you need to analyze data in your transactional applications (Salesforce, Oracle, SAP, etc…)

Business Scenario
A business analyst would like to analyze order data to improve on-time shipments, but when he exports order data from SAP, the hundreds of tables sent in spreadsheets are far too complex and unwieldy.

Technology
Data Warehouse (DW), Extract Transform & Load (ETL)

Transactional applications store data in a format optimized for transactions (e.g. recording an order). This format is difficult, if not impossible, to utilize for analysis, due to the complexity and performance impact of analyzing large numbers of tables and joins. A BI platform extracts the data from these applications; transforms it into a format optimized for analysis (star-schema), and loads into a data warehouse. The star schema is a format that
takes thousands of transactional tables and converts them into as few as 10 analytical tables optimized for analysis. The data is combined into Facts (numbers) at the center of the star and Dimensions (qualitative descriptors of facts) as the points of the star. (Example: Order Revenue = Fact, Order Date = Dimension.) The data warehouse serves the purpose of holding this data and any other data from other applications that you wish to analyze.

Why Care?
The business analyst no longer spends hours in Excel trying to analyze orders. Instead they simply ask the key question: What stage in my order process is slowing my most important orders? For which products? In which regions?

When Don’t You Need It?
1. If you need to analyze a single data source that has fewer than 10 tables, are not concerned with tracking historical performance or the data source already contains business metrics.
2. You are a solo data analyst who is the only one doing analysis and you know all of the tables in your transactional application and have the ability to perform rules and calculations on that data... and you have lots of free time!

My Opinion
DW and ETL are BI foundations. It's very clear that when we want to implement BI we need to build ETL and DW. This will become problem because customer need to invest and spend extra money. During my visit in some company in Indonesia, mostly say they don't need it for now because they have invest in human resource to do the analysis.

Question 2: Do you need to analyze data from multiple
different sources?

Business Scenario
A financial analyst wants to identify and remove bottlenecks from her company’s opportunity-to-cash process. The data lies across both its ERP and CRM systems,
but the analyst can’t bring the data together because key dimensions (like customer and product) don’t match across the different systems.

Technology
Data Warehouse, Conforming Dimensions, Data Integrity Logic

Dimensions like Customer and Product are represented in various formats and tables in different applications and sources. However, an analyst simply wants to address the question with respect to the customer regardless of the data source. To solve this problem, data from different sources is transformed and brought into the warehouse via a single dimension called a “conformed dimension” so that there is only one, single record for each customer. This process requires technology that supports data integrity logic so that the same customer does not appear as two different representations (i.e. P&G vs. PandG
vs. Proctor & Gamble).

Why Care?
Most business processes span multiple data sources—so it is difficult to get a single view of business metrics and terms that span these data sources. Your business needs a single version of the truth—with one view of the customer, one view of product hierarchy--and you can achieve that with proper data integrity logic and conforming dimensions. In the above business scenario, these technologies would enable the financial analyst to identify the specific bottlenecks for a customer and/or product, because she doesn’t have to
worry about combing unlike data across sources or having two different representations of a customer or product.

When Don’t You Need It?
1. You already have a Master Data Management system (which is likely part of a data warehouse) that ensures there is only one view of the customer across your applications.

My Opinion
In Indonesia, most of the company has island of system or application. Most of the system is not integrated one another and they not have master data management. So what are they doing to resolve this issue? They map the systems manually in excel. Very interesting right?!

Question 3: Does your organization undergo sales territory alignments, job changes, mergers, or other organizational restructuring?

Business Scenario
A sales rep “rolls-up” to the Central Region in Q1. In Q2, sales territories are re-aligned and the sales rep now rolls-up to the West Region. When Sales Operations analyzes the regional sales performance for the first half of year, the sales rep’s numbers roll up to West, because that is her current region, however, her numbers should be in Central for Q1. Sales Operations has to either manually manipulate exported data or report incorrect values for
Central and West sales performance in Q1.

Technology
Data Warehouse, Slowly Changing Dimensions

Dimensional data (like sales rep region or job position) change frequently, but are important for analyzing business performance over time. Data warehouses handle this issue by turning the dimension (like region) into a slowly changing dimension, so that metrics (like sales) are properly compared to the dimension hierarchy as of the time that the metric (sales) is relevant. Beware of platforms that support slowly changing dimensions through one-off coding or scripting as they require more maintenance, very specific scripting skill sets, and are subject to errors in scripting. More sophisticated BI platforms will support
these concepts as an integral part of their architecture.

Why Care?
The one constant in business is change. Without slowly changing dimensions, you are presenting analysis that is not only inaccurate, but possibly resulting in wrong decisions. Analyzing data is as dangerous as it is powerful, which is why you should ensure your BI platform can handle changes in dimensional data. As a result, as your business grows and flexes, your data can keep up with your state of constant change—and your sales rep’s numbers are accurate, even if Illinois moves to California.

When Don’t You Need It?
1. Your organization or dimensional data does not undergo any changes and you don’t want to analyze data in the past. If this is the case, you probably do not need a BI platform and Excel should suffice.

My Opinion
Just like the last sentence above say : "Excel should suffice" and even when the customer when to analyze data in the past, they will using excel too. Also I think create this kind of DW is not easy, you have to understand the concept. Check my previous post about the Data Warehouse (DW).

Question 4: Do you need to compare performance today to snapshots of performance in the past?

Business Scenario
The VP of Sales wants to remove key bottlenecks in his pipeline and needs to analyze the revenue value and time duration of opportunities in each stage of the sales process. Unfortunately, his CRM solution does not provide this data, so the VP of Sales is blind to key bottlenecks in sales cycle.

Technology
Data Warehouse, Historical Snapshots

Historical snapshots capture data from transactional applications that are constantly changing. These snapshots are stored in the data warehouse as part of the facts and dimensions, so that business users can access the snapshot data quickly for analysis. For example, pipeline value in sales stage 3 one year ago, or same month last quarter. Some BI platforms may store this data in separate files or tables, which limits the reach of each snapshot since it is specific to a single analysis. This also requires technical maintenance since a new file is needed for each snapshot and the complex snapshotting logic is not easily handled with simple scripts. Some BI Platforms support historical snapshots as an integral component of the platform.

Why Care?
Very often the best way to make business decisions is to compare historical performance with current performance to help predict or influence future performance. Without snapshots, items that change in transactional systems are lost and can’t be analyzed. This analysis is vital to identifying trends and patterns and key to understanding if performance is improving or locating the root-cause of performance issues. Most importantly for the business scenario above, you want your VP of Sales to know how to remove the bottlenecks in the pipeline and decrease the sales cycle time.

When Don’t You Need It?
1. You do not need to analyze historical performance, and you don’t care
about improving future performance.

2. You already have a data warehouse that is capturing historical snapshots.

My Opinion
Excel still can do this! I think without BI and DW, excel can do this easily. This question I think is not powerful enough to drive the customer for buying the BI.

Question 5: Do you need to apply business rules or logic to data for analysis?

Business Scenario
The Director of Demand Generation would like to analyze and compare Social Media with traditional lead sources. However, the social media data (flat files) comes with little structure while other lead source information and internal website data have different data structures.
The Director of Demand Generation needs a common way to analyze her various lead sources, yet there is no consistency to any of the data. As a result, the business has no
visibility to the value and impact of its social media efforts.

Technology
ETL, Data Warehouse, Logical (Semantic) Layer

Data comes in various formats, including flat files, cubes, and relational databases, storing both unstructured and structured data. The data warehouse can store all of this data and organize it in a format that enables analysis, but the data may not be in a language (metrics/ business terms/rules) that business users can understand. The Logical Layer applies business meaning to warehouse data and defines how the various data elements relate to each other. It provides a robust way to create metrics and business rules that are not
apparent in its data sources. The information represented in the logical layer is often called metadata. For example, a piece of metadata may be a single fact called number of touch points that applies specific logic to social media, email, web, and traditional lead sources to create a single business metric upon which a marketing analyst can run all touch point analyses. Even if you have a data warehouse, the logical layer is still required to provide business meaning and logic to all that raw data and empower business users to query
the business metrics and dimensions.

Why Care?
Raw, unstructured data can provide valuable business insight, but it requires translation before a business user can analyze it. A Logical Layer gives that data business meaning without reliance on IT. For example, wouldn’t it be nice to know the business impact of your social media spend and compare it to traditional sources?

When Don’t You Need It?
1. Your data already incorporates business meaning, required metrics, and business rules.
2. You’re a database guru and prefer to run SQL queries directly on top of the data.
3. You are a data scientist performing cutting edge machine learning analysis, which means you probably don’t need a BI platform, but a data mining engine instead.

My Opinion
I think when we talk about unstructured data, we must talk about hadoop too. It's a component that we cannot separate from the unstructured data. Big company like telco, consumer good and financial services are the company that need to analyze the unstructured data (social media, web, etc...) They have money and solid foundation (system) to move forward to "BIG DATA" era. In other words we can say that, the company that it's business model is B2C need to consider this soon, since they have to do sentiment analysis related their product or branding.

Question 6: Do you need to distribute professionally formatted reports to executives, customers, or other organizations?

Business Scenario
The Director of Customer Service wants to generate and distribute a customer facing service performance report on a weekly basis that has a specific format for all customers and sensitive data tied to each customer. However, creating and distributing these reports would require one full time employee to manually manipulate data from three different data sources, match Customer IDs, format the report using Excel, and apply data security to generate reports just for his top 10 customers. This manual reporting method is neither scalable, nor professional in appearance, nor consistent over time.

Technology
Pixel-Perfect Banded Reporting

Enterprise reporting is much different than dashboards. Ensuring that a report meets professional standards requires specific technology capabilities for formatting, distribution, data security, parameterization, bands, sub-reports and other capabilities. Pixel-perfect reporting has deep requirements beyond simple analysis and charts and your organization should confirm that your specific reporting needs can be met with the platform you select. Furthermore, pixel-perfect reporting is part of a complete BI platform that will grow with
your organization and enable you to leverage your investment beyond your initial use case.

Why Care?
When the CEO asks to see the data in a specific way (and that way only), you want to be sure you can provide that information to him consistently. Furthermore, you want to provide a professional image of your company when you distribute reports externally, especially to customers.

When Don’t You Need It?
1. Your analysis needs are limited to a very small group and your enterprise reporting needs are already being fulfilled with another solution.
2. Your organization has very low cost resources who can create reports based on data from transactional systems and you can hire and retain more of these resources forever.
3. Your organization does not mind if the appearance of the reports vary from run to run.

My Opinion
Some of enterprise BI already provide this feature (Pixel-Perfect Branding Reporting). If the customer need this feature too instead of visualization only, then go with it. But if the customer need only for the reporting, I have heard that there are some products out there that provide this functionality or feature. Of course the price will be different!


Question 7: Does your organization have employees with different analytic skill levels?

Business Scenario
A data analyst wants to explore and ‘play’ with data—filtering, pivoting, and visualizing—while the VP of Sales demands a single dashboard showing her most current pipeline and order analysis.

Technology
Advanced Visualizations, Dashboards, and Ad-hoc Analysis

Among the most difficult and complex issues in analyzing is providing the right tools to allow users of different skill sets to properly visualize insights. A flat, static dashboard with non-interactive charts does not provide enough detail, nor the slice and dice or ad-hoc capabilities for the business analyst; while a complete blank canvas ad-hoc tool will be useless or overwhelm users who need access to simple reports. Robust ad-hoc tools allow business analysts to pick dimensions and measures to answer specific questions (I.e. “Tell me the departments where more than 20% of managers had performance review scores of 5 or higher”). Robust dashboards provide advanced visualizations through a diverse chart library and data exploration capabilities like drill paths, drag and drop filtering, column selectors, filters, prompts and pivoting— while providing these options in a view that does not overwhelm users with too many options.

Why Care?
This is where the rubber meets the road. Varied and strong dashboards along with visualizations ensure employees gain access to the rich insight they need to make decisions on behalf of the business. Easy to use ad-hoc analysis provide business analyst quick answers to question instead of spending hours manipulating data in excel. For the scenario above, you want to provide the VP of Sales with her clean pipeline dashboard while at the same time, enabling the data analyst to delve deep into the data.

When Don’t You Need It?
1. Your users are a few data analysts who only need ad-hoc analysis ; your organization has no need to disseminate rich insight.

My Opinion
Yes, this is what every company looking for. Self service analytics is very popular right now. All of the departmental BI already offer this. Simple, intuitive and easy are the keys for selling. Excel still good but who doesn't want a tools that help them do this easier? No one! They want and need this, but there is a major concern for this: some of the customer want the dashboard printable easily. It is easy to print but not easy to customized like excel.


Question 8: Do you want to show reports or charts within the context of an existing application?

Business Scenario
The sales team receives half of their commission after the customer has paid the company. Each sales rep wants to understand customer payment history and know when each order has been paid. The sales operations analyst cannot provide this information, because the volume and format of order data from the ERP system is unusable, and she still has no way of disseminating the information in a timely fashion with proper data security. She would like to display a trend analysis of customer payment history and order detail in the Salesforce.com account page, but has no ability to do so.

Technology
Embedded Analytics and Row/Column Level Security

Once data is prepared for analysis (i.e. housed within the data warehouse and logical layer) and shown in a visually compelling way (i.e. reporting and dashboards), the expectation is often that the project is done and you’re your users have all that they will need. However, the best way to drive business value from analysis is to make analytics part of your daily, ongoing business processes. In order to do this, your BI platform needs to support embedded analytics, which includes capabilities like security, authorization, APIs, iFrames,
and UI customization, so that reports or dashboards can be displayed inside a transactional application—all while keeping data secure and providing a consistent look and feel. Keeping data secure requires row/column level security, which apply rules about who can access specific data, based on various properties such as geography, title, and type of customer.

Why Care?
Putting analytics in context and securing the data is among the best ways an organization can take action on the insights contained in reports and dashboards. In the above business scenario, the company has the ability to reduce their days sales outstanding, by giving sales reps visibility into their customer payments. Having such analysis embedded directly into the rep’s Salesforce.com account page, gives the rep the ability to drill into exactly
which orders are un-paid and do something about it.

When Don’t You Need It?
1. The data you are analyzing does not live inside an application your company uses and has no value within other applications your company uses.

My Opinion
This question is related with the question no 2. Customer has to prepare the data first (DW) and then search for the tools for read the data from the DW. The BI site is only about the security (row / column level security) because the security in the DW level is not advance to handle this kind of request but some of DW products already provide this kind of security. 

Question 9: Does the organization need to perform “what-if” planning? Or project future performance?

Business Scenario
A supply chain analyst would like to understand the impact of increasing the inventory re-stock value on shipment performance and inventory obsolescence. She would like to utilize past performance data on shipments and inventory as a proxy; however, with data across multiple product lines and from various systems, she is unable to perform the analysis. Without all data together in a single model, she cannot perform the “what-if” analysis to
determine which levers to pull to improve business performance.

Technology
Projection Analytics, “What-if” Modeling

“What-if” modeling does exactly what is says. It uses historical data to build a model and enables a business analyst to pull specific levers (change data) to project future performance. This projection analytics can be accomplished with a data warehouse, logical layer, and business rules that allow a data analyst to model the business relationships. These models use simple relationships between historical data (e.g. Project future closed revenue using a model of pipeline close rate based on sales stage and sales rep). They are not to be confused with Predictive Analytics models which a data scientist builds to predict future performance based on statistical relationships between data.

The advantage of “What-if” modeling is that it can provide guidance and direction to a business analyst, without hard core data mining. It can also help a business user understand order of magnitude differences between different levers that can used to have the greatest impact on business.

Why Care?
The primary reason to analyze historical data is to drive future decisions for better financial performance. “What-if” modeling and projection analytics do just this, and put the power in the hands of those who can use it, business leaders. For the example, empower the supply chain analyst to perform “what-if” analysis on supply chain levers so they know what options they have to increase on-time delivery without adversely affecting inventory levels.

When Don’t You Need It?
1. You are analyzing data that cannot be used to model future occurrences. An example, would be a one-time event that will not be repeated by your business, such as emptying your bank account to pay $4M for a super bowl ad.

My Opinion
When I tried to sell the BI technology, most of the company will ask about this feature. What-if my sales increase 20% and the cost reduce 5%. How about my sales trend? Can this tools provide an insight and predict my revenue? Questions like that is often asked by the customer. Don't forget that the customer need to have sufficient and correct data in order to generate the prediction.

Question 10: Do you need to perform in-depth analytics that relates data from seemingly unrelated sources or tables?

Business Scenario
The marketing director would like to perform a cohort analysis on subscription revenue to determine hot spots for retention and analyze this against recent social media and press releases from company. However, renewal data is in the ERP system and social media data is in flat files completely unrelated to the ER  data. This requires analyzing order date with renewal date and factoring in product across both of these dates—while adding business context to social media data.

Technology
Complex SQL, Multi-pass SQL, composite keys

Why leave this topic to #10? This topic may be the hardest to grasp, and least understood by BI vendors, implementer, and customers alike. Business users often need to see a relationship between two pieces of data, while the data itself has no technical data join. To solve this problem, a relationship needs to be created, that is correct analytically, while ensuring analysis can be done quickly. This requires establishing a shared dimension to join two tables that do not share a common column, a logical layer to create business meaning
between the two different sources, and multi-pass SQL that allows a single question to be parsed into multiple questions against different data and bring together the answers logically. It sounds complex—and can be—however, a platform that supports these capabilities will do it without a user knowing. And, most importantly, it will do it in a way that answers are returned for these tough questions correctly and quickly. Platforms that mimic multi-pass SQL or infer relationships without shared dimensions/composite keys are subject to returning incorrect results and can cause more harm than good.

Why Care?
A business user should not have to worry about where the data lies or how it is structured. If a business user wants to understand how social media and press releases impact renewal rates, they should be able to do that without knowing how to spell composite key or Multi-pass SQL. A platform that correctly solves these issues, while shielding business users from complexity of data modeling can drive rapid business value. In the example, if analysis reveals that positive social media sentiment increases renewal rate, then the business now has a new lever to pull to improve financial performance.

When Don’t You Need It?
1. If your analysis is simplistic and on a single data set.
2. If understanding how external factors impact your internal business in unimportant to grow your business.

My Opinion
To be honest, I'm not quite familiar with this question. I haven't done any "real" project related with the social data directly. I have some experience about analyzing social data, it is about sentiment analysis in Twitter and use Wisdom in Facebook. It is very interesting but I still don't understand this question. I get the point : its about connecting 2 different data (in this case ERP and Social Data) but the question no 10 might be handled by Hadoop and ETL before being processed in the BI tools. Please correct me if I 'm wrong. Thank you.



Source : 2014 Birst Top 10 BI Business Scenarios