SMEs are so caught up in the day-to-day management that they often fail to plan or predict what the future may hold for them. This is where analytics, which is the buzz word around the world today, should play an increasingly vital role.
Three terms: Analytics, Business Analytics and Business Intelligence are usually used interchangeably. Analytics is a term commonly applied to all disciplines, not just business. Analytics can be defined as a combination of data processes by using statistical methods like central tendencies like mean, median, mode, variance, standard deviation, graphs, and so on.
If this is getting too complicated, allow me to break it down into simpler terms. There are three broader categories of analytics that are accepted as standards. Firstly, descriptive analytics tells us about “what happened” that is; it points to historical data to yield useful information and possibly prepare for further analysis.
Second, is predictive analytics, a more advanced version of analytics which helps forecast “what might happen” using possible techniques and methods from data mining, statistics, modeling, machine learning, and artificial intelligence to analyze current data and to make predictions about future.
Thirdly and lastly, comes prescriptive analytics, which tries to prescribe the best course of action for a given situation out of multiple options available and tries to optimize on “what should be done“.
Integrating business methods and techniques
In the present era, SMEs are thriving despite the cutthroat competition and regulated marketplace, and therefore, business people have to think one step ahead of their contemporaries. A little prediction goes a long way.
Today, leaders driving growth may not know what predictive modeling, forecasting, designs of experiments and mathematical optimization mean or do. But for sure, over the next decade or so, it is going to become their mainstream business aiding computing techniques.
Ginnie Rometty, current Chairwoman, President and CEO of IBM has rightly quoted in this context, “Every part of your business will change based on what I consider predictive analytics of the future”.
When any organization, however big or small, goes live with predictive analytics, it starts on a journey of making profound changes to the way business is done. It touches or interacts with the operations and people associated with it, either directly or indirectly.
Within these interactions, initially process goes unnoticed till someone bothers to look at how it is adding up for them. The improved decisions may each be small-sized, relatively speaking, but there are so many that come to a powerful net effect. Guided by predictions, this improves millions of small decisions within an establishment. SMEs launching predictive analytics into action, deliver a decisive new edge in the competitive world.
At a time when companies especially belonging to the SME sector offer similar products and use comparable technologies, high-performance business processes are among the last remaining points of differentiation.
SMEs are drowning in data but starving for information. Now “nice to have” applying analytics, especially business predictive analytics is becoming mission critical for them.
Preliminary predictive analytics techniques for everyone
Predictive data science offers a large body of algorithms from its constituent disciplines, namely statistics, mathematics, operations research, machine learning and scientific computing.
Data Analysis techniques, do not necessarily seek fancy and highly advanced tools or applications, to churn out data stories. To my understanding, the top commonly attempted and easy predictive analysis techniques are:
i. Correlation and Regression Analysis
ii. Cluster analysis
iii. Time series Analysis
iv. Classification
v. Association analysis
Excel: A useful tool in the realm of predictive analytics
The core of predictive analytics relies on capturing relationships between explanatory variables and the predicted variables from past occurrences, and exploiting it to predict future outcomes.
Using native Microsoft Excel worksheet functions, formulas and tools, can help in building basic analytics models. One can comfortably computer data central values, variances, standard deviations, correlation coefficients, regression analysis and many more, course with permissible dataset size. And on top of it, if we apply Excel’s add-in tools (like Solver, XL-Miner) one can turn out Microsoft Excel into full-fledged predictive analytics platform, that one can get from SAS, R, or any similar application designed specifically for statistical analysis. Furthermore, when one builds the analysis on its own, the results could be more of interest.
Although, excel does not offer a tool that automatically determines the best method to forecast from a given baseline of data and then apply that method on your behalf, it does give the possible tools to make that statistical determination and use its results to build predictive forecasts.
To act is to decide: Only knowing is not enough
Developing a predictive model is not enough but it demands actions. Knowledge is not power, knowledge is only potential power and action is the real power of Predictive Analytics domain.
Some critical business areas where Predictive Analytics has proven its worth, beyond doubt are customer prospecting, customer retention, sales forecasting, cross-selling & business segmentation, production & capacity planning, among others. Wider the area to which an SME applies predictive analytics, the greater the potential impact.
By acting on the predictions produced -may be simple at first instances-SMEs can apply what has been learnt, modifying its everyday operations for all-round betterment. Predictive analytics remains different from many technologies because its impact is readily apparent and quantifiable. When SMEs starts seeing good numbers to a specific technology such as predictive analytics, it can easily see the value in adopting it.
Source: The Economic Times