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
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
Although now I am not focus on Analytics / Business Intelligence, I still have interest about it. I also have subscribed to TDWI since long time ago. It is a very good website to improve our insight about Analytics. Today, I have read about the Analytics trend and I would like to share it below. At the bottom section of this post, please see my review for the trends.
Several interconnected trends in analytics are relevant for companies looking to advance their analytics efforts. They include the following:
1. Ease of use.
Analytics in the past, especially more advanced analytics, often required command-line code. Today, vendors have made interfaces easier to use and visualizations easier to construct. Preparing and blending data has also become easier. And emerging automation techniques for more advanced analytics enable the software to actually suggest a model using the variables of interest and an examination of the data. This increasing ease of use means organizations can succeed early and then build on that success to become more
data driven.
2. The democratization and consumerization of analytics.
Connected to ease of use, more organizations are “democratizing” BI and analytics to enable a broad range of non-IT users, from the executive level to frontline personnel, to
do more on their own with data access and analysis via self-service BI and visual data discovery.
Part of this trend also involves making analytics more consumable (i.e., more accessible to different parts of the organization) often by operationalizing or embedding analytics into a business process. See trend #5.
3. Business analysts using more advanced techniques.
Also connected to ease of use is the move from the statistician/ modeler to a new user of predictive analytics—the business analyst. These analysts are using more sophisticated
analytics techniques such as predictive model building. They might build relatively straightforward models. They may collaborate with the statistician to build the model or validate it, or other controls may be put in place before the model is productionalized. This often frees the data scientist/statistician (typically a scarce resource) to build more complex
and sophisticated models.
4. Newer kinds of analytics.
In addition to predictive models, other kinds of analytics are emerging to drive business
value. These include text analytics (analyzing unstructured text), social media analytics, geospatial analytics (analyzing location-related data), and clickstream analysis (analyzing customer behavior on websites).
All of these techniques are starting to become more mainstream and can provide important insight, either by themselves or in combination with other techniques. Typically, the more mature an organization’s analytics efforts are, the more it makes use of newer forms of analysis.
5. Operationalizing analytics.
When you operationalize something, you make it part of a business process. Operationalizing analytics is important because it helps make analytics more actionable and hence drive more value. For example, a statistician might build a predictive model for churn. The model is then embedded in a system, and the model scores customers as they call in. Based on this score, information flows to a call center agent as part of a business process—
say, to up- or cross-sell or take other measures to retain the customer. The agent doesn’t need to know how the model works but can make important use of the output for business advantage. Operationalizing analytics also helps make it more consumable.
6. Big data.
Referring to ever increasing amounts of disparate data at varying velocities, big data is the buzzword du jour. However, it is much more than that. An important point about big data is that it is driving the use of existing techniques as well as the development of new techniques for data analysis.
Big data is also driving the use of newer infrastructure such as Hadoop and multi-platform data warehouse environments that manage, process, and analyze new forms of big data, non-structured data, and real-time data. This might include NoSQL databases, DW appliances, and columnar databases. Other technologies such as in-memory analytics
are also gaining steam. Leveraging big data processing tools allows analysts to perform queries on larger data sets—providing more robust models and deeper reports—and avoids sampling errors that might occur with smaller data sets.
7. New development methods.
Unlike with BI reporting, analytics often demands that users explore the data and try
different visualizations and analytical techniques before they can arrive at insight. Analytics thus often demands a different methodology from what has been used for traditional IT projects to develop applications. Instead of “waterfall” methods and cycles that only deliver at the end of (usually) one long cycle, many organizations are employing agile methods. These faster, incremental cycles have helped guide organizations toward greater business-IT collaboration, faster and more iterative development cycles, and ultimately higher quality and satisfaction.
8. Open source.
Open source is rapidly becoming more popular for infrastructure as well as analytics. Hadoop is a prime example of how these technologies are becoming important in analytics. Commercial distributions of Hadoop are becoming more powerful. On the analytics front, the emergence of the R language is also evidence of the growing popularity
of open source. Many analytics vendors are already incorporating support for R into their packages. The open source Python programming language is also increasingly popular for analytics. Open source is important because it enables the rising innovation happening around the analytics ecosystem.
9. The cloud.
Although it has taken longer than some expected for the cloud to be used in BI, it is now entering the mainstream. One reason organizations are trying to move toward the cloud is to offset costs with zero capital expenditure on infrastructure, maintenance, and even personnel—often making BI more cost-effective. Additionally, deployment is faster. Organizations are making use of various types of cloud deployment and delivery options for BI and analytics. For example, data generated in the public cloud is often analyzed there as well. This analysis might be basic or complex. More often, companies are capturing big data in the cloud and experimenting with it there. Based on the analysis, certain data is brought on premises to the data warehouse.
10. Mobile BI and analytics.
The increasing adoption of mobile devices has opened up new platforms from which users
can access data and both initiate and consume analytics. Executives on the go can apply analytics to gain deeper insight into business performance metrics, while frontline sales and service personnel can improve customer engagements by consuming data visualizations that integrate relevant data about warranty claims, customer preferences, and more.
11. Storytelling.
As analytics and advanced analytics becomes more main stream, being able to tell the story with analytics is becoming an important skill. A data story—a narrative that includes analysis—can move beyond recounting of facts to weave together pieces of analysis that make an impact and move people to action. TDWI is seeing two different kinds of data stories emerging. The first is the one-time storytelling with a classic beginning, middle, and end. This is often a presentation style of storytelling which includes a call to action at the end. A more modern kind of storytelling is dynamic and often changes through time. It might use some sort of online dashboards or storyboards that are updated when new data arrives. The analysis is typically shared with others who comment and build an iterative and often
interactive story.
...
For no 1 (ease of use) I think it is very true. I have see several product roadmap and their major concern is to make it easier but with more rich features or functionality. For no 2 (The democratization and consumerization of analytics) and no 5 (Operationalizing analytics) in Indonesia is very challenging. It is already happened since long time ago, so it is not a new trend here in Indonesia. Most of the company won't invest new resource for data scientist so they will optimize their current staff to do operate the BI. At the same time, the existing resource (staff) will try to automate their operational things by using the Analytics / BI, so they don't do the "double" job.
For no 3 (Business analysts using more advanced techniques) no 4 (Newer kinds of analytics), no 6 (Big data) and no 8 (Open Source), I think is relate to each other. Basically to understand and learn more about the unstructured data, we need big data. Things like text analysis, sentiment analysis and social media analytics are something unstructured. We need hadoop for that. Today, most of the analytics Product also integrate with 3rd party application to enhance their BI, one of them is R (Statistics). To operate all that things above, we need something that much more advance than a business analyst, it is Data Scientist. The amount of data will be massive but it contain insight that could be revealed by Data Scientist. Most of the big or giant company in Indonesia has start moving to this part of Analytics. They have money to invest and demand to fulfilled.
For no 7 (New development methods) is true! We cannot wait for the full enterprise Data Warehouse (DW) fully ready, so we try to build the analytics using agile approach. Usually the approach is by Data Mart (Slice of DW). The user also want to try as soon as possible their analytics.
For no 9 (The cloud), to be honest I think it is not suitable here in Indonesia. Most of company are not the comfortable to store their data in the cloud because of the is strictly confidential. When we talk about the analytics investment, they are looking for the cloud solution, but for the realization I don't know why the discussion always end with the data protection and regulation.
For no 10 (Mobile BI and analytics) it is a mandatory. The user want mobile! They are doing business and analysis using mobile. If you want to sell BI / Analytics, sell the mobile feature! It is work and most of the customer attracted by that kind of things.
Last review for no 11 (Storytelling). I don't know exactly what is this about at the first time. But from these sentences, you will understand : data story—a narrative that includes analysis and the analysis is typically shared with others who comment and build an iterative and often interactive story. It is sentences or words that appear on the dashboard to guide the user for read the charts / objects in the dashboard. It is very useful but you have to create it simple in order not to make the screen messy.
Thank you for reading, hope it will be useful!
