Can Small Companies Justify Investing in Business Analytics?

By Bill Cabiró

Even in the case of very small businesses, a strategically deployed Business Intelligence solution can have a major impact on the growth and profitability of the company.

Having a clear view of the profitable customers, products, regions and market segments is fundamental to understand the causes and expand upon the successes.

Equally important is to find those customers, brands, markets, segments and competitors responsible for draining cash to quickly stop the bleeding.

Should we treat all customers equally? 

If you say yes, I’d suggest reading “Angel Customers & Demon Customers” by Larry Selden & Geoffrey Colvin.

This is because in a company that has more than thirty customers, the Pareto Principle will be evident.  Looking inside the Pareto’s famous 80/20 rule not only one can find that the top 20% of products contributes 80% of total sales.  

We can also find that the top five percent of the customers generates close to 50% of the profits while the bottom 50% of the customers generates only 5% of the total profit.  Even worse, the bottom 40% usually does not generate any profit at all!

Many companies have a hard time identifying which customers belong to each group.  The good news is that the strategic use of Business Analytics provides these seemingly difficult answers instantly.

Regardless of the business situation management should be able to ask critical performance questions and find -first hand- clear answers at the speed of thought without interruptions or the involvement of analysts:

• Is revenue growing profitably? Where? How and Why?
• Are we paying sales reps commissions for bringing in unprofitable tonnage?
• Can we quickly tell whether the growth trend is just over last month, last quarter, same quarter last year or this year to date?
• How about the profit growth of the last 52 weeks compared to the previous 52 weeks?  Is it really growing?
• Is our growth accelerating or decelerating?  How much?
• How is our profit growth versus budget or business plan? Where are we failing to meet our objectives?  Why?
• Which competitors are threatening our business?
• In what regions and market segments can we maximize our growth?

A modern Business Intelligence solution is fundamental to find answers to the seven layers of WHY's in order to get to the root cause of issues.  Being able to understand and correct these issues faster than the competition provides the company a competitive advantage regardless of how small the business is.

In the past deploying a Business Intelligence solution was not affordable by small companies, not only due to the high licensing cost of the software but also because of both the internal and external resources necessary to set it-up and provide maintenance.

This is no longer the case.  Today, there are many choices of analytic applications, either on premise or in the cloud, that are powerful, user friendly and very inexpensive.  

Designed for business users, they include interactive data discovery and self-serve visual analytics that require minimal IT involvement, making them ideal to bring small companies to the forefront of the 21st century technology to become true analytic competitors.

References:

1)    Angel Customer & Demon Customers by Larry Selden & Geoffrey Colvin
2)    The 80/20 Principle by Richard Koch
3)    Competing on Analytics y by Thomas H. Davenport, Jeanne G. Harris
4)    Strategic Knowledge IQ Test

Image: GraphicStock

What Is Business Intelligence 3.0?

Interactive Visual Analytics represents what Gartner Research calls the consumerization of Business Intelligence.  It’s a good example of a disruptive innovation or, as Qlik’s Donald Farmer calls it, a market changer.

According to Tableau Software “visual analysis is not a graphical depiction of data. Virtually any software application can produce a chart, gauge or dashboard. Visual analytics offers something much more profound. Visual analytics is the process of analytical reasoning facilitated by interactive visual interfaces”

This new generation of BI tools is so intuitive to the regular user that little or no training is necessary to explore data. This is a great feature since, according to Gartner, BI users do not want to read manuals. They demand intuitive BI interfaces, in line with the internet experience they are accustomed to, like Google searches or smart phone apps.

The current low BI utilization rate of about 5% does not provide many companies an acceptable return on their Business Intelligence investment.  The new user-friendly and intuitive visual analytic tools are helping companies exploit the treasure of business trends, patterns and opportunities hidden in their oceans of data by increasing the number of employees that participate in the data discovery process. The technology enables casual users to transition into power-users and power-users into app developers in a matter of weeks.

Depending on the vendor, the new software class is known by different names: Data Discovery, Advanced Visualization, Visual Analytics, Business Discovery, Self-Serve Business Intelligence or Business Intelligence 3.0

Advanced data visualization is based on the fact that 70% of the human sensory receptors are dedicated to vision while the other four senses share the remaining 30%.  In addition, our brains are much more effective recognizing shapes trends, patterns and colors than analyzing spreadsheets or tables full of numbers. Visual data analysis principles are based on the work of Edward Tufte and Stephen Few.

The data visualization market begun to grow during the last decade as business users started purchasing these applications for departmental use, mainly without IT consent.  The reason was simple: business analysts needed the capability to analyze all sorts of data rapidly, beyond the scope of the data-warehouse.  A request that most IT organizations were not prepared to fulfill. Today as data discovery tools have become more popular and scalable IT organizations are more involved in the purchasing process. 

Today more than two dozen applications fall into this category from companies around the globe. The leading ones –Tableau and QlikView- have grown their acceptance at a very fast rate.  Ten visual applications have made it to Gartner Research's 2015Magic Quadrant for Business Intelligence and Analytics and this number will grow in the future as more BI solutions continue to add visual analysis functionality.  Gartner estimates that more than half of net new purchasing is data-discovery-driven.

"For years, data discovery vendors — such as QlikTech, Salient Management Company, Tableau Software and Tibco Spotfire — received more positive feedback than vendors offering OLAP cube and semantic-layer-based architectures.  In 2012, the market responded:

·        MicroStrategy significantly improved Visual Insight.

·        SAP launched Visual Intelligence.

·        SAS launched Visual Analytics.

·        Microsoft bolstered PowerPivot with Power View.

·        IBM launched Cognos Insight.

·        Oracle acquired Endeca.

·        Actuate acquired Quiterian".

To be clear, Self-serve BI does not mean “IT Free” as a strong IT-Business partnership is always helpful to ensure data quality through proper governance and also to maintain the proverbial single version of the truth.  Self-serve refers to the user’s ability to perform data exploration and discovery simply by clicking or tapping into interactive dashboards and reports.  All this without having to request IT to create specific data marts, build OLAP cubes or predefined reports as this would delay the data analysis process.

Also, it’s important to highlight that there are two kinds of self-serve BI user: 

1. Analytics Power Users who create visual apps from multiple data sources –both internal and external.
2. Regular Users that can fully explore the visual apps created by power users or IT.

In addition to the typical functionality of multidimensional analysis (drill-down, drill-through, roll-up, sort, group, filter and calculations) some visual tools offer “what-if” scenario analysis, data animation, integration with the statistical ”R” program and mobile capability. 

A great feature of this new generation of BI software is its data blending functionality. These applications allow connecting simultaneously to disparate types of data bases or tables, whether in a data warehouse, data-marts, spreadsheets, text files, Microsoft Access, websites and in the case of Tableau OLAP cubes.

The analytic results are instantaneous since the process takes place in-memory (RAM).  Additionally the visual interface, when used proficiently, permits to digest huge amounts of information and visualize trends and patterns in seconds.  This process enables what many call “analysis at the speed of thought”; Meaning that the answers to business questions can be found fast enough without  interrupting the “train of thought” that leads to the next layer of questions, seeking to find the root cause of issues or opportunities.

Visual Analytics packages are not replacing traditional BI in large organizations but complementing them. They provide fast analytical capabilities to more people that need to gain a competitive edge in the current fast changing market dynamics.

For small and medium size companies that haven’t yet invested in BI, Self-serve, Visual Data Discovery is a cost effective solution that can be deployed very fast.

This new generation of BI software takes descriptive analytics to a whole new level. That's the reason for the fast growth rate this $4.5 Billion market segment has experienced during last few years.

It only takes a few minutes to download the free versions most vendors offer for testing purposes.

Below: Dr. Hans Rosling’s video is a few years old but still illustrates the power of data visualization techniques that make 120,000 data-points tell a compelling story in a way that’s very easy to understand. 

Note: The original version of this article was published here by Bill Cabiró in 2011.

Image: GraphicStock

Are You a Business Intelligence Avoider?

From six different studies we can conclude that approximately only 5% of employees use BI tools to perform Analytics effectively.

I think this is in part related to the fact that except for folks with backgrounds in science, engineering, economics or finance; many people across the company do not feel too comfortable around numbers, math or logic functions.  I've observed this during years of training corporate employees on the strategic use of Business Intelligence and Analytics.

This is how the numbers work: close to 50% of employees have access to BI tools, about 20% of them actually use BI, and about half of them (5%) are in the analytical / power user categories.

Despite recent inroads of the more user friendly BI visualization tools of the last few years, the BI Scorecard’s annual survey of users, administrators, and directors reports flat utilization rate since 2006.

If you were to plot a histogram showing only those 20% actual BI users in an organization, it would probably approach a normal distribution (bell shape curve) consisting of the following categories, where close to two thirds of the users would fall in buckets 3 and 4.

1) Non Users: Run canned reports once a quarter or less frequently. These are people who are either math averse; do not like computers, or are executives that have their assistants run and print static reports for them.

2) Infrequent Report Users: Run canned reports about once a month.

3) Frequent Report Users: Run canned reports on a weekly or daily basis.

4) Analytic Users: Modify static reports and OLAP cube views by grouping, sorting, formatting as well as changing some dimensions and measures. These folks save their new customized reports for future use.

5) Power BI Users: Create new ad-hoc reports from scratch applying multiple dimensions, measures and using grouping, sorting and filters. These users travel interactive dashboards and OLAP cubes from corner to corner using drill down, drill through, and most of the available custom features in search of the root causes of both: problems and opportunities.  Power users create and share reports, dashboards and visualizations with folks in the same department.

6) Expert Analysts: Search find and provide new data bases, blend disparate data, design and build customized cubes, pivot tables and dashboards, perform statistical, financial or marketing analyses and usually export results to MS Excel to complete the last analytical mile.  Expert analysts create and share reports, dashboards, visualizations and analyses with management across the organization.

Buckets 4, 5 and 6 represent the 5% of employees that use BI tools to perform analytics effectively.   When it comes to Advanced Analytics (statistics, predictive modeling, data mining, etc.), data scientists or statisticians are probably close to 10% of that number or just about 0.5%.

To become a true analytic competitor, the company has to change the culture so everybody, not just the experts, thinks and acts based on facts and understands the drivers that support strategy and sustainable profitability. 

While this isn't easy, it’s possible.  I've seen it quite a few times.  It requires long term commitment from top management, adequate training, data structured to be business-intuitive and an interactive visual analytic software tool configured in an extremely user-friendly manner so all types of users can perform analytics with virtually no help from IT, analysts or even spreadsheets. 

In my experience, the organization improves its financial results through this implementation as people gradually advance to the next level, leaving buckets 1, 2 and 3 practically empty. 

Which buckets is your organization using most?