I need business analytics…do I?

Analytics….a thrilling new domain, continuously explored by statisticians and data lovers.

But raw data is like an uncut diamond which will become more precious the more it will be burnished. Data analytics is the art of gaining an in-depth view of the company with a view to guiding it in its future course based on information stored within the organization.

A proper assimilation, dissection and analysis of data can help figure out the most obvious challenges or questions within an organization.  It can help to determine what went wrong (or right), and correct course, understand the performance of the organization better, and therefore plan better.  Data analysis is best used in making strategic decisions and business planning for future.

There are a number of research reports which are pointing towards the growing importance of business analytics – business analytics and optimization is a $105 billion market that includes hardware, software and services, featuring a growth rate of 8%

Top Business Analytics uses

 

 Business intelligence in data analysis is very important as it operates all the raw data dealing to each and every aspect of the organization, then to organize those data with the available tools. Data analysis helps to organize large chunks of data into fields and columns which can be conclusive for the need of company and the data analyst.

Data analysis can help in fighting downturn as it helps to analyze the trough well in advance. It helps in studying the market conditions supporting or not supporting the business.

A last word to all companies who are still nagging to be analytics driven:

“83% of Chief Information Officers (CIOs) of companies identified business intelligence and analytics as their top priority for enhanced competitiveness.”

Who needs Analytics?

The push for greater use of analytics is unfortunately more from the analytics and BI service and solution providers rather than from the end user.  This lop-sided advocacy results in a severe mismatch between the actual need for, and the felt or expressed desire for analytics in the day-to-day management of business.

There continue to remain  a number of skeptics within the industry who do not make use of analytics.  Some typical reasons that are quoted are:

“I believe in myself and have full confidence in my ability to view my business. I have full control over my company’s operations. I do not need anyone else to tell me what is right!”

“I have done it in the past, and it worked.  I know that if I do the same things again, it will work again.”

“Numbers are used by people who want to hide the truth. If you want to see reality, you must roll up your sleeves and be in the centre of action; not behind some computer, remote from the ground.”

“An external advisor will never know as much about my business as I do.  What new perspectives can I expect to gain from him?”

“Data is confusing. Give it to me straight – can you tell me what to do, or can’t you?”

And then there is always the lurking fear in the senior manager that he or she will appear to be less knowledgeable.  What then will be the security of his position within the company? Will others see him as being less competent?

In the context of the increasing requirement of depending on multiple intelligence sources to be able to make correct business decisions, this is a breed which is quickly moving towards fossilization

Most CXOs speaks the same language:  when the business is running well, they do not see the need to invest in analytics, and when the business does not do well, they do not have the back-up to be able to make the difference.

Now here comes the need of analytics oriented approach – although companies panic to appoint analytics teams and to bear the incremental cost incurred on their facilitation, the benefit of having it in place, especially in times of need, far exceeds these costs.  In the face of growing manpower and IT costs,  companies tend to internally analyze and identify their needs and try to solve their issues based on past experience and future anticipation. Under such circumstances, they see analytics as an unwanted load which can be dispensed by short term measures. Such stop-gap measures will often result in failure and the inability to make more out of the information at hand.

But is it possible for any company to transfer the data from the memory chip of one set of people to another? In order to accumulate the memory of all the memory chips one needs to create a system and an approach to work with information (that is analytics!).

Why do these businesses fail in the long run?

It is their overconfidence or the lack of ability to anticipate the needs in a rapidly changing world that mainly leads to failures.  Some of the reasons for not being able to manage businesses are:

ü  Loss of data in absence of appropriate data capturing method

ü  Lack of clearly identified goals

ü  Poor integration of data from multiple sources

ü  A betting decision maker (“I will wager that this is the right answer!”)

ü  Lack of in house analytical skills demands for proper analyses

ü  Lack of clear performance measures across organization

Let us face it, the world today requires many more business leaders than the rate at which they are being produced.  Naturally, the quality or the ability of leadership suffers.  But this need not result into the business suffering, so long as these leaders realize that there are tools and methods available to enable decision making.

BI use drivers

The imperatives to use data for business are many, and some of the larger companies have started to make use of them.  These companies are driven by the fact that their margins are being squeezed, their ability to generate incremental revenues is being limited, and their overall performance has reached levels where they cannot expect to continue to grow unless they improve their performance.  Some of the reasons why these companies have chosen to go for business analytics are:

For smaller companies, managing their data environment is much simpler..but if they do not start this early in the life of their companies, they are likely to hit a ceiling when they are looking to accelerate.

This blog is also posted on: http://mydatawise.com/i-need-business-analytics………………do-i

Missing Data

I have witnessed so many cases of data either being mis-utilized, under-utilized, or simply not being used at all that I thought that it is important for me to talk about it in this blog. Companies fail at making the best out of information which they already have. Sometimes this could be simply because during their day-to-day activities they just do not get the time to be able to review data, or perhaps they do not realize the importance of doing some basic data analysis. Where companies do tend to use data, I see broadly four types of problems associated with the use of data:

1. Data analysts are so overwhelmed with the data available to them from various sources that they spend an eternity trying to unravel the data and make some sense out of it. Data, if not analyzed timely, loses all its meaning. Agreed, it is not always easy to analyze data or to make meaning out of it. But then, to someone who understands the data, attempting to make sense out of it is fairly simple. Some of the easy methods of being able to make sense out of data without wasting too much time on its accuracy are:

• Using approximations for data which could simplify the analysis

• Using surrogates against data which is not easily available or retractable

• Simplify the data analysis problem itself.

2. Managers are not clear about what they want. Problem definition is sometimes so weak that it is impossible to conduct an intelligent data analysis around the problem. For data analysts, there is no clear answer to a question such as, say, “ What do I do if…?”. Rather, data analysts are at best able to suggest what could happen if…. The what-do-I-do question can best be answered by decision makers. It requires both an understanding of the direction of the data, and the applicability of the date to the given solution.

3. Data analysts are prone to derive conclusions which are spurious. A good example is the one that is most often quoted with regard to the correlation between ladies hemlines and the stock market index. Again, data analysts who do not have a complete business understanding of the situation are likely to arrive at incorrect conclusions. They could possibly derive a meaning out of two sets of data where possibly none exist, leading to fallacious conclusions, leading to loss of confidence of managers in the analysis, or a lack of a well directed analysis.

4. And that brings us to the last of the concerns with data usage. Managers typically manipulate data to suit their conclusions. The entire purpose of data analysis is then lost since the end result is already known to the manager, and all conclusions are necessarily biased towards establishing his or her own point of view.

So, what should data be typically used for? There are a few simple, and not-so-earth-shattering things which should be done:

1. Look to data to seek a confirmation of what you as a decision maker have in mind.

2. Be as precise as possible in setting the question that you would like the answer for. The more precise the question, the greater the likelihood that you will be able to get a meaningful answer to your question from the data.

3. Do not expect data analysis to tell you what to do. As a manager, you must make the decision yourself. You can, at best, rely on data analysis to give you directions as to what to do.

4. Finally, double check the analysis from at least 2 or 3 different viewpoints to confirm that the answer you have is a meaningful one, and that you are not being misled by incorrect data analysis.

To assume that data analysis is the cure for all ills is a fallacy. At the same time, to decide not to rely on the wealth of information already available with you is a crime.

This blog is also posted on: http://mydatawise.com/missing-data