Tag Archives: BI

5 Ways to Drive Value with BI Proof of Concepts

by Kaan Turnali, Global Senior Director, Enterprise Analytics

Designers in a meeting --- Image by © Laura Doss/CorbisProof of concepts (POC) specifically designed for business intelligence (BI) projects can be invaluable because they can help to mitigate or eliminate the risks associated with requirements whether we’re working with a new BI technology, asset, or data source.

POCs (sometimes referred to as proof of principle) may be presented with slightly varying interpretations in different areas of business and technology. However, a BI POC attempts to validate a proposed solution that may cover one or more layers of the BI spectrum through a demonstration with a small number of users.

There are many reasons why a BI POC may be needed, and they may come in different shapes and sizes. Some focus on the end user; others may deal with data or the ETL process. BI POCs can be small, quick, and even incomplete. Or they can be involved, measured, and lengthy. Some are initiated ad-hoc and executed informally while others may require a process as strict as a full-scale project and the same level of funding as a formal engagement.

Here are five ways to drive value with your BI POCs.

1. Focus More on the Value and Less on the Mechanics

You can’t lose the sight of the big picture—it doesn’t matter how simple the BI requirement may appear or how informal the process may be that you’re asked to follow. Often BI teams concentrate on the technical details (a necessary step), but you need to go beyond just the mechanics and think about the value. Sometimes, a technical solution alone may not be adequate because technology is only half of the solution. And BI is no different.

2. Identify All of the BI Layers in Question

In a typical BI project, there are usually several layers involved: data, ETL, reports, access, and so on. Depending on the size and/or scope of your BI project, identifying the correct BI layers that need validation becomes critical. For example, you may be looking at a report design, but you can’t simply ignore the underlying data source or required data transformation rules.

3. Cheat on Sample Data, but Not on the Logic

Time is an extremely scarce resource in business, and POCs are often executed at a higher velocity. As you manage the process, it’s completely acceptable to cut corners such as hard coding a value in a report instead of fully defining the formula or building an integrated process to calculate it. But if you cheat, you should always cheat on time and not on concept.

4. Define and Manage the Scope

No matter how informal your BI POC may be, you need to define and maintain a POC scope. Open-ended or prolonged efforts result in waste. And BI POCs are not immune to this virus. It may not require intricate project- or change-management processes, but you still need to have a plan and execute around that plan.

5. The Right Talent Matters

Identifying the right talent with the right background is critical to your BI POCs success. It goes without saying that subject matter expertise around BI as well as areas related to business content and processes is a prerequisite. However, equally important are the soft skills, starting with critical thinking.

Bottom Line

If our goal is to enable faster, better-informed decisions, technical know-how alone won’t guarantee successful outcomes, because a POC is only as good as its assumptions and the BI team that’s executing it.

It all starts and ends with leadership that can pave the way for executing a BI vision where technology becomes a conduit to delivering business growth and profitability through the talent and passion of our teams.

What other ways do you see that can drive value with BI POCs?



The Human Aspect of Predictive Analytics

By Ashith Bolar , Director AmBr Labs, Amick Brown

The past decade and a half has seen a steady increase in Business Intelligence (BI). Every company boasts a solid portfolio of BI software and applications. The fundamental feature of BI is Data Analytics. Corporations that boasts large data do indeed derive a lot of value from their Data Analytics. A natural progression of Data Analytics is Predictive Analytics (PA).

Think of data analytics as a forensic exercise in measuring the past and the current state of the system. Predictive Analytics is the extension of this exercise: Instead of just analyzing the past and evaluating the current, predictive analytics applies that insight to determine, or rather shape the future.

Predictive Analytics is the natural progression of BI.

The current state of PA in the general business world is, for the most part, at its inception. Experts in the field talk about PA as the panacea – as the be-all and end-all solution to all business problems. This is very reminiscent of the early stages of BI towards the turn of the century. Hype as it may be, BI did end up taking the center stage over the ensuing years. BI was not the solution to the business problems anymore; it was indeed mandatory for the very survival of a company. Companies don’t implement BI to be on the leading-edge of the industry anymore. They implement BI just to keep up. Without BI, most companies would not be competitive enough to survive the market forces.

Very soon, PA will be in a similar state. PA will not be the leading-edge paradigm to get a headstart over other companies. Instead PA is what you do to just survive. All technologies go through this phase transition – from leading-edge to must-have-to-survive. And PA is no different.

predict the future

Having made this prediction, let’s take a look at where PA stands. Some industries (and some organizations) have been using predictive analytics for several decades. One such not-so-obvious example is the financial industry. The ubiquity of FICO scores in our daily lives does not make it obvious, but they are predictive analytics at work. Your fico score predicts, with a certain degree of accuracy, the likelihood of you defaulting on a loan. A simple number, that may or may not be accurate in individual cases, arguably has been the fuel to the behemoth economic machinery of this country, saving trillions of dollars for the banking industry as well as the common people such as you and me.

Another example would be that of the marketing departments of large retail stores. They have put formal PA to use for several years now, in a variety of applications such as product placement, etc. If it works for them, there is no reason it should not work for you.

This is easier said than done. Implementing Predictive Analytics is not a trivial task. It’s not like you buy a piece of software from the Internet, install it on a laptop, and boom – you’re predicting the future. Although I have to admit that that is a good starting point. Implementing the initial infrastructure for PA does require meticulous planning. It’s a time-consuming effort, but at this point in time, a worthy effort.

Let’s take a look at high-level task list for this project

  1. Build the PA infrastructure
  2. Choose/build predictive model(s)
  3. Provision Data
  4. Predict!
  5. Ensure there’s company-wide adoption of the new predictive model. Make PA a key part of the organization’s operational framework. Ensure that folks in the company trust the predictive model and not try to override it with their human intelligence.

Steps 1 thru 4 are the easy bits. It’s the 5th step that requires a significant effort.

Most of us have relied on our superior intellect when it comes to making serious decisions. And most of us believe that such decision-making process yields the best decisions. It is hard for us to imagine that a few numbers and a simple algorithm would yield better decisions than those from the depths of our intellect.

However, it is important to change your organization’s mindset about predictive analytics. If you are considering your business to be consistently run on mathematical predictive models, acceptance from the user community is crucial. Implementing PA is a substantial effort in Organizational Change Management.

Remember the financial services industry. They don’t let their loan officers make spot decisions on the loan-eligibility of their clients based on their appearance, style of speech or any such human sensory cues. Although, if you ask the loan officer, they might claim to be better judges of character – the financial industry does not rely on their superior human intellect to measure the risk of loan default. Generally, a single 3-digit number makes that decision for them.

The next time you go to the supermarket for a loaf of bread, and return with a shopping cart full of merchandise that you serendipitously found on the way back from the bread aisle including the merchandise along the cash-counter, you can thank (or curse) the predictive analytics employed by the store headquarters located probably a thousand miles away from you.

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Artificial Intelligence meets Business Intelligence

By Ashith Bolar, Director of Research, Amick Brown

It’s bound to happen:  Artificial Intelligence (AI) will meet Business Intelligence (BI). In fact, in several places, it has already happened. But let’s take some time to see how this convergence is progressing, if at all.

The first decade of the 21st century was all about Business Intelligence. Towards the end of the decade, big strides were made to harness the explosion of Big Data. The second decade has been mostly about fuelling Business Intelligence with the Big Data. Several companies, large and small, have been making very impressive strides in this direction. However, there is still a lot of room for improvements.

On the other side in the world of computing, Artificial Intelligence has been making slow inroads in all aspects of life. In the last 15 years, AI has been creeping up into our personal lives with applications such as Siri, the entire Google ecosystem, and a myriad of social networking applications. All of this is happening without us realizing the amount of AI happening behind the scenes. Artificial Intelligence has moved out of the academic realm towards the daily lives of consumers.

Much of the business community associates AI with machine learning algorithms. While that’s true, it leaves much of AI underappreciated for its real capacity in Business Planning and Data Analytics. There is more to AI than just recommending your next movie on Netflix and making Google give you better results on your web search.

There are several applications and platforms that transform and summarize a corporation’s big data. However, ultimately it’s the humans that consume this summary of data, to make decisions based on higher human intelligence. I argue that this will change over the next decade. If history is any indication of how accurate our predictions of coming technological revolutions are, I would imagine this transformation will happen much sooner than a decade.

Most of us know Big Data and the Internet of Things that has enabled this explosion. Big Data infrastructure does much of the heavy lifting of cleaning up, harmonizing, and summarizing this data. However, the actual process of deriving intelligence and insights is still within the human realm.

Inevitably, the future of Business Intelligence goes hand in hand with Artificial Intelligence.

The new wave of BI software should be able to perform the basics of building data analytics models without human intervention. These systems should be able to generate hundreds of models overnight. The next step is to build systems that not only generate redundant set of models, but also identify the good models – ones that model reality accurately – and weed out the bad models. The third wave of solutions will be the ones that make a majority of decision making for a company.

In the coming posts, we will explore in more details some of the initial attempts of converging Artificial Intelligence and Business Intelligence.

Data Driven Decisions improve your business

Fact Based Decision Making

For many companies the first reporting and analytics question that they ask is, “What specific items should my company measure?” However, what you measure should be based on how to get results from your data that make measurable change in the organization. The first question really is, “What are my business goals and what measurable components can help me achieve or miss this goal?

Decision Man

Some examples are:

  1. Churn reduction
  2. Retirement possibilities in the next year.
  3. Employees that leave in less than a year
  4. Departments with the highest attrition
  5. Supply chain service improvement
  6. JIT miscalculations by department or location
  7. Customer service complaints, late deliveries to customers
  8. Staffing variations and what affect this has on production 

Why fact based is important…

Another huge common occurrence in the reporting world is decisions made without trackable, measureable fact. Unbelievably, companies still make decisions with historical process, experience, and their “gut” to some degree.

With the availability of big data, your competitors are not only going to have access to information about themselves, but also about your customers and your ability to perform. If this data is not used well by your company – you will lose business.

So how exactly does your company aggregate data, produce reports, or glean insight to meet goals? If you are like the vast majority of companies out there, it is a big data dump into a spreadsheet that is picked through and interpreted by the individual who requested it.

Many have very slick Reporting solutions, but they are not leveraging them to the full potential – not by a long shot. Why is this? The pervasive gaps are that people are creatures of habit and continue to want to report like they have always done and change management/training is not factored in. The poor IT Manager put in charge of the BI project is inundated with data dump requests and help requests on-going.

Circling back to Data Driven Decisions, the very first things that must be carefully and completely determined are:

  1. What are the challenges that prevent me/my company from beating the competition, increasing revenue, operating smoothly, etc?
  2. What people drive the resolution of these challenges?
  3. What data and report metrics do these users need to show the golden road to overcoming the challenges?

Beginning with what decisions need to be made, which people will drive goal attainment, then what data and metrics will roll up to an answer – the first big hurdle to Business Intelligence success will have been overcome.

For more on this subject, watch this space… blogs.amickbrown.com/ 


Top Three Hurdles to Successful Reporting and Analytics

This is a first conversation based on what I am hearing in the market. There will be more to come, and I want your thoughts please. Let’s make a difference. 

Top Three Hurdles to Successful Reporting and Analytics

The challenge of useful, powerful, and appreciated Business Intelligence is felt across industries, departments, and roles. By using BI well, you will position yourself to beat your competition. If you do not use the data available to drive business decisions and goal attainment, you position your competitors to win – because they ARE leveraging their data.

What is the definition of successful Business Intelligence?

My best practices definition is “Success is measured by the ability of the right people, to use the right data, and create usable reports that aid in business goal attainment”.

Sounds simple, right? Well it will be with planning, understanding and buy-in from users at all levels. It is truly a change management issue as well as a technology issue. IT will drive the technology side, but must work hand in hand with the various business leaders to develop outcomes that make a difference in efficiency, process, and profitability.

The Top 3 Hurdles to BI Success:

  1. “Give me all of the data and I will figure out what I need”

Users, Managers, and Executives do not realize the depth of business case resolution that their data can provide. The approach tends to be, “give me all of the data and I will figure out what I need and want to use.” Inherently, this is manufacturing the outcome instead of letting it manifest organically.

Tied closely to this request is the real situation that people do not like change. They “have always done it this way” is a first cousin to the data dump method. Overcoming a historic process can be harder than learning how to use BI well.

With the powerful BI tools available, dashboards and reports can be targeted to achieve business success. These successes will be defined by each leader based on corporate goals. The tough part comes in taking a measurable goal and allowing the solution to mine the data from various sources to provide accurate reports from which to make decisions. Long story short – is the report authentic and actionable.

  1. “My data is a mess ! “

How many times have I heard that reporting and analytics is a moot point because the data flowing in has not been cleansed or integrated in years. Well then, we know where to start because this statement is true. Garbage in is garbage out.

So, this hurdle to BI success becomes part of the solution. Regardless of how simple the reporting and analytics outputs are, their foundation must be in valid data.

Housekeeping is essential – so the longer cleaning the house is put off, the dirtier it will get.

  1. One and done is not an option

Let’s look at a very common situation: When the shiny new “box” of BI software came – the enthusiasm was real. Users throughout the company were vested and interested in the cool reports that they would be able to generate. Well, that was 8 years ago. Hopefully much has changed in your business since then. The reports, however, have not changed. You are measuring and dwelling on 8 year old business challenges. This is definitely not effective.

A proactive sustainability plan will separate the average performing BI users from the rock stars. Incorporate this into your reporting and analytics plan!


This is a first conversation based on what I am hearing in the market. There will be more to come, and I want your thoughts please.

 The challenge of useful, powerful, and appreciated Business Intelligence is felt across industries, departments, and roles. By using BI well, you will position yourself to beat your competition. If you do not use the data available to drive business decisions and goal attainment, you position your competitors to win – because they ARE leveraging Big Data.