In the last decade, the world has experienced significant technological advancements enabling the explosive rise of social media, online businesses, mobile devices (smartphones, tablets, and smart wearable devices), electronic commerce, airspace technology (drones, in-flight entertainment, etc), software and hardware evolution, amongst others. These technological improvements have impacted the way we communicate with one another, entertain ourselves, transact business, report events, behave in the workplace, and our overall style and quality of life.
In Nigeria, businesses now take advantage of technologies to improve performance. For example, it is now easier and cheaper to advertise products and services on websites and social media platforms such as, Facebook, Twitter, WhatsApp, Instagram, LinkedIn and blogs.
Increasingly, businesses are adopting alternative service delivery and payment platforms. These businesses also have systems which are used to record, authorise and process transactions with internal and external parties – staff, customers, suppliers, vendors, trade partners, regulators, etc.
The use of these technologies and platforms create an avalanche of information; which constantly produce data about customer personalities, preferences, buying patterns, geographical locations, behaviours, cultures, potential future purchases, etc. These are sometimes done intentionally but at other times, unintentionally. In the past, these data were never stored because of huge costs and technological limitations in capturing, storing and processing them.
However, it is now easier and cheaper for businesses to keep both master data (customer names, customer reference numbers, home addresses, phone numbers, email addresses, dates of birth, nationalities, and so on) and transaction data (such as price and quantity of products bought, locations where businesses were transacted, methods of payment, delivery methods, names of the staff who dealt with which customers, payment preferences, among others) in databases.
Moreover, businesses have opportunities to obtain and store customers’ reviews of products and services on their websites and social media platforms. For example, data relating to what web pages they visit, how long they stay, when they leave, product/service ratings by the customers, can be extracted and stored.
Research shows that about 90 percent of current world data was generated in the last two years. An increasing amount of data is becoming available on the internet across multiple sites and in different forms. This is where the concept of big data originates from.
Big data is an evolving term that describes the voluminous amount of structured, semi-structured and unstructured data that has the potential to be extracted for information, intelligence and value. Business executives have realised that the gigabytes and terabytes of data generated at ever-increasing speed in our digitised world have created an urgent data analytics imperative.
Data analytics is the use of tools and techniques to collect, organise and analyse large sets of data from disparate sources to discover patterns, trends, relationships and useful information, which hitherto were never stored or locked up in remote archives or wasted away at the point of data generation.
Organisations are tapping into increasingly sophisticated analytics techniques to improve opportunities for growth, innovation and competitive advantage. Applying analytics technologies, tools, techniques and talent can transform dry facts and figures into strategic insights that deliver intelligence in the moment. You can now solve specific and complex business problems with reliable information rather than depend solely on your gut instincts.
With the application of data analytics, businesses will better understand their customers and therefore, be able to predict their future buying patterns. For example, a retail business that sends a new product SMS advert to all its customers may find that only 5 percent of the customers need the new product. In this instance, the business could have saved 95 percent of its advertisement cost if detailed analytics was performed on the customers to determine their preferences. Most of the time, information required for such analytics resides in the company’s database or the archive or in unstructured form in different data sources such as social media. For instance, if a customer has ‘liked’ a particular product on Facebook, there is a higher likelihood that this customer may be interested in the product.
Another example of the application of data analytics is in the area of cost minimisation. Imagine a large organisation whose workforce reduced by 20 percent over a two-year period and the cost of providing staff lunch increased by 15 percent within the same period, while inflation rate only increased by about 5 percent. This is an interesting finding that requires further investigation and when followed through, will result in cost minimisation. Likely issues may be fraud or errors in the recording of the expenses and these can be addressed by putting appropriate controls in place.
Likewise, an organisation can use data analytics’ techniques to detect fraud in transactions. For instance, an insurance company can define several fraud indicators and apply these algorithms on submitted claims before they are processed. The algorithm will leverage both internal data (from the insurance company databases) and external data (from shared information from other insurance companies, social media, and other sources).
The use of data analytics in government is huge as the volume of information available to them is considerable – from hospitals (birth registration, patient registration, death registration), to examination bodies (primary school leaving certificate, senior secondary school examination, unified entry examination, and so on), to company registration, voters’ registration, tax registration, land ownership registration, car licensing, etc.
The information gathered and analysed from government can be used to plan for the provision of hospitals, transportation and road networks, employment, schools, as well as track tax defaulters, profile people to identify criminals, etc.
Data analytics will help business managers determine the actual cause(s) of sales’ decline rather than just attributing it to something as vague as a harsh business climate. For example, a business can reliably estimate that 5 percent of its total 20 percent sales’ decline is caused by seasonal variation in demand, another 7 percent is caused by a drop in the quality of products/services, while the remaining 8 percent is due to a newly introduced product by a competitor. With this insight, a business can more easily determine the next steps required for an increase in the volume of sales.
Similarly, a business with increasing sales can apply data analytics to determine which of its products are doing well and why. This will also help to determine which of the products need to be improved on.
Data analytics find extensive use in financial/operational audits, fraud prevention/detection, revenue and expense assurance, crime detection, competition analysis, government planning and resource control, governance, risk and compliance (GRC), etc.
From the industry perspective, data analytics can be applied in telecommunications, financial services, automobile, aviation, power and utilities, entertainment, health care, pharmaceutical, insurance, oil and gas, construction, real estate, consumer and industrial products, transportation, public sector, etc.
Data analytics is not an end in itself but a means to an end. With data analytics, insight is translated into business decisions with appropriate actions taken to improve the business performance such as reducing cost, improving revenue, managing risks, measuring performance, complying with regulations, etc.
An organisation that does not tap into the opportunities presented by the implementation of a data analytics programme, runs the risk of having untimely (late) and unreliable information for decision making. The consequences of these include customer dissatisfaction, loss of market share, competitive disadvantage, declining revenue and profitability, loss of brand value, amongst others.
The reality is that the world has moved on and traditional ways of doing business are no longer sustainable in this era of data explosion – adopting a data analytics solution is the way to go.
Saheed Bashiru
Bashiru is senior manager at PwC.
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