Is Analytics Ever Enough?

Data analytics has come a long way from the days of a small team of statisticians poring over spreadsheets, graphs and pie charts in the backroom offices of giant corporations. As technology advanced, so did the methods of gathering and the sources of data. This phenomenon caused a great upset to the traditional methods of data analysis. Vast amounts of data were coming in so thick and fast that even the most brilliant statistical minds were overwhelmed. As early as 1962, John W. Tukey, published an article titled, The Future of Data Analysis. In it, he stated as follows; “Data analysis, and the parts of statistics which adhere to it, must…take on the characteristics of science rather than those of mathematics, data analysis is intrinsically an empirical science.” Many years later, these words have proven to be quite prophetic.

A look into the past

In December 1999 Jacob Zahavi was quoted in; “Mining Data for Nuggets of Knowledge”, a publication by the Wharton University of Pennsylvania as stating that; “Conventional statistical methods work well with small data sets. Today’s databases, however, can involve millions of rows and scores of columns of data. Scalability is a huge issue in data mining. Another technical challenge is developing models that can do a better job analyzing data, detecting non-linear relationships and interaction between elements. Special data mining tools may have to be developed to address web-site decisions.” Modern day analytics applies these special data mining tools.

Modern day analytics has evolved greatly from the purely mathematical affair that statistics has always been, to a multi-facet discipline combining mathematical and scientific tools with technology. The advent of the technological age saw the rise of computers which could compute vast amount of data within a very short span of time. The interaction between computer science and statistics brought forth the modern day version of analytics. This has brought about the tremendous growth of a vibrant data analytics software industry. Businesses and companies globally have adapted the use of various analytics software to help them figure out trends in their sector and insights that can help them improve on their business performance.

Critical review of analytics

But with the memory of the 2007/2008 global financial crisis being still fresh in our minds, the question still begs, is analytics ever enough? Pretty much every business now has a way of gathering data and turning them into analytics. This is supposed to eliminate the ‘noise’ and create a clear path for success. If it is enough, how comes so many businesses were caught flat footed by the crisis, despite having a wealth of data and insights at their disposal well before the crisis started? If it is not, why do so many businesses expend so much time, effort and finances on analytics, if they do not offer a guarantee of returns? Join us as we embark on this journey of discovery as we seek solutions to the query, is analytics ever enough.

The problem that most modern day businesses experience with regards to analytics stem from the fact that businesses have abandoned sound business practices for quick fixes based on data analytics. Here is the thing; although analytics can provide very useful insights on business trends, only a professional equipped with the right tools of analysis and interpretation can utilize the information for the business. Analytics are akin to a high powered sniper rifle and ammunition. In the hands of a skilled sniper, the same can be applied in a hunt with deadly accuracy and efficacy. Put the same rifle in the hands of an amateur, and you will be lucky to get a hit on your target.

Analytics has turned business into a game of numbers. However, we are questioning whether the deep understanding of a business is getting overlooked in the clamor to ensure that you have the best insights possible. Does it take things like industry experience to really be able to put forward actionable steps? Using the illustration of the sniper rifle, are we investing so much effort and time in ensuring that we get the best ‘rifles’ possible, without having in place trained ‘marksmen’ who can utilize the rifles. So, in the midst of all this confusion and noise, how do you handle the issue of analytics in your business?

A study of the application of analytics in business indicates that they play a very important role in business. Properly done analytics can inform the short term strategic goals of a business, allowing it to take advantage of the trends in the best way possible. But in order to achieve this, the insights have to be dependable and reliable, and deduced from real data.

The weakness of data analysis is that it is likely to be affected by the bias of the person interpreting the data. Our view and perspective in life influences our approach of issues in a great way. In the same way, data analysts are likely to add their biases when analyzing data. If they want to find a trend, sure enough they will find one and provide tons of data to back it up. A deep understanding of the business is thus important to ensure that the insights provided are justifiable and actionable. Otherwise you may end up with a lot of hot air couched in big words and decorated in flowery language as insights, but gain zero benefit from the application of the same.

Looking forward

Analytics by itself is never enough. The insight to action journey is punctuated by numerous curves, pit stops, speed bumps and barriers that may hinder the effective application of insights to achieve some desired results. As of now, one of the key ways of ensuring this is by certifying that the results of analysis can be implemented in a controlled, documented, measurable manner. While you may need analytics to make smart decisions, you also need to be smart to execute the insights provided by analytics correctly, in order to benefit your company or business. At the end of the day, all analytics does is provide information, on which basis we can make informed decisions. By all means delegate the data analysis part to the faster and more effective technological platforms, but ensure that at the end of the day, you do not delegate the decision making part of the business to analytics. That, is still your responsibility.