Business Analytics in the age of big data

 

Running a business in the last century was much simpler, entrepreneurs such as shop keepers or a butcher knew all their customers tastes, likes and preferences and were able to meet those needs.

Globalization, Technology, population growth etc have made the life of many growing companies in Africa quite challenging. Many SME’s have been forced to device ways of knowing their customers using other means other than face to face conversation and information sharing.

Take the above example of the shopkeeper who has grown his/her business to be a big retail chain suddenly he/she realize that is very difficult to interact with customers as much as they would want to. If they are not careful he/she may lose touch with customer preferences and likes and therefore lose market share and valuable customers.

The art of learning and re-learning customer behavior, tastes, likes, preferences etc using whatever tool be it observation, interviews, survey can be loosely be termed as business analytics.

In Africa today, companies are using the availability of cheap and easily accessible information (Big data) to make decisions.

Using data to support decision-making is not new, and falls under the umbrella of business analytics.

The difference now is that one can collect much more information about any element relevant to the decision-making, thanks to the ever-decreasing costs of data collection, storage and processing.

The entrepreneurs with a retail business for example can collect a diverse range of information such as customer demographics (gender, location, and age), weather, real-time inventory information from RFID (radio frequency identification) chips, and even blog post and video reviews of products.

 

Traditional business analytics can be classified as

• Descriptive,
• Predictive
• Prescriptive analytics

Descriptive analytics takes available data to describe what is happening. An important aspect of doing descriptive analytics well is in the presentation of information. Google Trends is an excellent example of visualizing search term popularity by region and by time.

Predictive analytics consists of using past data to forecast the future, and is routinely used in all aspects of a business.

Prescriptive analytics, uses past data and a decision model to reach actionable recommendations.

Whereas descriptive and predictive analytics require the presence of a human manager to interpret the results, prescriptive analytics allows for automated decision-making, as long as the decision model is decided upon a priori.

 

What Does the Future Look Like For Business Analytics and Big Data?

Remember that traditional business analytics have always used data. For example, suppose you were a bookseller and needed to make stocking decisions from the publisher in advance. Under small data analytics, you would collect historical sales data, observe any trends in the data (e.g. higher sales during the holiday season) and perform a time-series forecast of the future demand. These are examples of descriptive and predictive analytics. You can also perform prescriptive analytics on the dataset by computing an order quantity that maximises the total estimated future revenue.

On the other hand, in the new era of Big Data analytics, you can collect not just historical sales data, but data on other features also associated with the demand.

Data Has a Voice, Let it speak

To extract value out of Big Data, you still need to perform descriptive, predictive and/or prescriptive analytics.

However, traditional analytics tools may no longer work due to the size of the dataset. Computations will be slower and larger memory will be required.

Nevertheless, descriptive and predictive analytics with Big Data are becoming more and more prevalent in a wide range of industries.

Hospitals and medical insurance companies are using electronic medical records to identify patients with higher readmission risks and determine premium payable, retailers such as Nakumatt, Shoprite and other chains perform targeted marketing by mining customer purchase data.

Whereas descriptive and predictive analytics with fairly large datasets have already been successfully deployed, prescriptive analytics with Big Data are at an early stage of development and are mostly confined to academic research.

Application of Big Data in Business

So how should you bring Big Data to the daily operations of your organisation?

First and foremost, there needs to be a diagnosis of the current analytics capability. If currently there is very little systematic data collection and analysis, then even doing small data analytics can generate a great deal of value.

Remember any organization irrespective of size can use business analytics using big data; you just need to start small, learn from your mistake and grow according to your needs and capability.

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