Relevance of business analytics to the strategies of an enterprise

In today’s technological world, the rate of change is so rapid that a lot of businesses are not able to cope. There is so much data available with the click of a button unlike the past when it took days to get such critical data to analysed for decision making.

The advancement in computing has also meant that these machines are taking decisions as humans. There has been a lot of debate about the ability of computers to take better decisions compared to humans but research has that is true due to the fact that the computer is about to deal with different scenarios with click of a button compared to the human mind.

Thus said, the intervention by humans in the process of decision making cannot be overlooked. Businesses have to become smarter now than before so they can take advantage of the available data to become more competitive and in some instances, use it as an edge to stay ahead of the pack.

Competitive advantage comes from capitalizing on uniqueness. Every organization is different and every organization has the potential to exploit that exact uniqueness in a way that no one else can match. Doing this means taking advantage of their single biggest resource: their data.

This therefore brings us to the conversation on the topic Business Analytics and its relevance in strategic decision making by an enterprise. Business Analytics has become an important tool in the decision-making process of a lot of businesses across the world.

The amount of data being churned out every day has become overwhelming and decision making can lag behind thereby making businesses uncompetitive.

The term Business Analytics can be defined in various ways but the preferred on for this article is “Delivering the right decision support to the right people and digital processes at the right time[1].” For the pace at which businesses are accelerating, forward looking organisations are beginning to realized that analyzing data alone is not enough.

They are beginning to operationalize their analytics as part of their business processes.  Embedding analytics in the business is about integrating different insights from within and outside into systems and business processes used in making decisions.

These systems might be automated or provide manual, actionable insights. Analytics are currently being embedded into dashboards, heat zones, applications, devices, systems, and databases. Examples run from the simple to complex with organizations at different stages of operational deployment.

Businesses are using the data to analyse and predict patterns and even to the extent of sending specific adverts to their customers based on the purchases pattern or behaviour in the past. Newer examples of operational analytics include support for logistics, investments, portfolio management, customer call centers, fraud detection just to mention a few. Embedding analytics is certainly not new but has been gaining more attention recently in helping business leaders make decisions. Business analytic can be placed in three major components

  1. Descriptive analytics – Most businesses start with descriptive analytics which is the use of data to understand past and current business performance and make informed decisions. These are typically presented in bar charts, histograms and in some cases trend analysis. For example, revenues, operation expenses, net incomes etc. Descriptive analytics is the most commonly used and most understood type of analytics. This process allows managers to obtain standard and customized reports and then drill down into the data and make queries to understand the impact of say purchases of raw materials from different regions. Typical questions that descriptive analytics helps answer are “How much material did we purchase in each region?” “What was our revenue and profit last quarter?” “Descriptive analytics also helps companies to classify customers into different segments, which enables them to develop specific strategies.
  2. Predictive analytics – Predictive analytics seeks to predict the future by examining historical data, detecting patterns or relationships in these data, and then extrapolating these relationships forward in time. For example, an investment banker might wish to predict short/long-term movements in stock prices, or a maize farmer might want to predict next season’s demand for maize. Predictive analytics can predict risk and find relationships in data not readily apparent with traditional analyses. Using advanced techniques such as algorithm, predictive analytics can help to detect hidden patterns in large quantities of data to segment and group data into coherent sets to forecast behavior and detect trends. For instance, a bank manager might want to identify the most profitable customers or predict the chances that a loan applicant will default, or alert a bank card customer to a potential fraudulent charge. Predictive analytics helps to answer questions such as “What will happen if demand falls by 10% or if supplier prices go up 5%?”
  3. Prescriptive analytics – Many challenges, such as vehicle or employee scheduling and supply chain design, for example, simply involve too many choices or alternatives for a human decision maker to effectively consider. Prescriptive analytics uses optimization to identify the best alternatives to minimize or maximize some objective. Prescriptive analytics is used in many areas of business, including operations, marketing, and finance. For example, we may determine the best listener strategy to maximize revenue as radio station, the optimal amount of cash to store in ATMs, or the best mix of companies in an investment portfolio to manage risk. The mathematical and statistical techniques of prescriptive analytics can also be combined with optimization to make decisions that take into account the uncertainty in the data. Prescriptive analytics addresses questions such as “How much should we produce to maximize profit?” “Should we change our plans if a natural disaster closes a poultry business: if so, by how much?”

In his book, “Principles” Ray Dalio, the Chairman and Co-Chief Investment Officer of Bridgewater Associates chronicles the way he used Business Analytics in making decisions about the commodity market in the 1970 to 1980s when computers were not as powerful has we have today. Through his leadership the organisation built a solid system that could make decisions which were in some cases better than humans. This did not just happen out of the blue. It was more of gathering data, analyzing the trends and using the trends to bet on the upside whilst reducing their risk exposure. The company by virtue of this competitive edge, predicted accurately the 2008 financial crises. A quote from Ray Dalio “Our flagship fund made over 14 percent in 2008, a year when many other investors recorded losses of more than 30 percent.”

Companies realize that making analytics programmatic by automating operational decisions can be beneficial to both the top and bottom line. Another big advantage of operationalizing business analytics is that it makes it more consumable. Consumability has become a hot topic because it makes analytics available to a wider group of people than simply those who analyze data or develop models and share it with a select few. As more people use analytic output, its value increases.

With respect to the job market by 2018, the McKinsey Global Institute predicts a national shortfall of 1.5 million qualified analysts and managers in the United States who can make sound business decisions based on an accurate analysis of big data. In Ghana, it’s likely that we have no idea about the shortfall in employment because we have also not seen a lot of businesses using business analytics in decision making. However, in January 2018 in the Daily Graphic in Ghana, there was a publication on Songhai Group signing an agreement with Fidelity Bank to develop the capability of the bank’s data modelling and analytics division. This is a huge boost to help management take decisions and its strategy to serve customers better and improve on its returns. This could help propel Fidelity to become a leading bank and will offer a learning opportunity for young people in the bank interested in Business Analytics.

Modern business analytics can be viewed as an integration of business intelligence/information systems, statistics, modeling and optimization.  For example, data mining is focused on better understanding characteristics and patterns among variables in large databases using a variety of statistical and analytical tools.

Simulation and risk analysis relies on spreadsheet models and statistical analysis to examine the impact of uncertainty in the estimates and their potential interaction with one another on the output variable of interest. What-if analysis is also used to assess the sensitivity of optimization models to changes in data input and provides better insight for making good decisions. Visualizing data and results of analysis provides a way of easily communicating data at all levels of a business and can reveal surprising patterns and relationships. Software such as IBM’s Cognos system exploits data visualization for query and reporting, data analysis, dashboard presentations, and scorecards linking strategy to operations. An example is recently, on a visit to a large-scale poultry operator and during the tour, I realized that, the company still hanged papers in front of the pen containing the birds to gather data. This data is then sent to the operation office every week. This means if there are any incidences of disease outbreak or low egg laying capacity, that may most likely be realized after a couple of days by which time probably the farm may have lost some revenue. In the same scenario, I asked the Production Manager if he is aware of the temperature in the pens, and his answer was negative. Knowing from studying agriculture that the rate of egg laying can be affected by the ambient temperature his answer was indicative that this was obviously overlooked.

[1] Laursen, Gert H. N. “Business Analytics for Managers: Taking Business Intelligence Beyond Reporting (Wiley and SAS Business Series)

Challenges with Business Analytics

For enterprise to develop business analytics they will need large volumes of data. However, sometime it is difficult to obtain large volumes of data and not question its integrity and quality. Other challenges that business could face can include model complexity and enterprises can be carried away whilst trying to achieve a perfect solution for the myriad of their problems. Model Usability is another challenge that can disrupt the relevance of business analytics. The model has to be one that can deliver true value for the company and management can get the best of it. There is one thing developing a model and another its usable by the parties relevant to its development.

 

In conclusion, the use of business analytics is not new, it’s the importance that management or leadership places on it that will set them apart. This require a very strong open discussion among the leadership of the organisation and to understand the role it will play in future decision making. Any plans to implement such a strategy will cause some pain and in some instances cost money and this can sometimes be higher than anticipated. The changes to decision making will be altered and it can be challenging at the beginning because mistakes will be made and through it new things will be learnt.

By Festus William Amoyaw – Portfolio Manager with Acumen Fund Incorporated

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