From gut-based to data-driven, decision-making transformed by COVID-19

Shakeel Jhazbhay, General Manager: Digital Business Solutions at hybrid IT systems integrator and managed services provider Datacentrix.

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The time to step away from instinctual decision-making is now. Today, a scientific, fact-based approach, leveraging insight derived from an organisation’s data, is critical to the navigation of the current business world. So says Shakeel Jhazbhay, General Manager: Digital Business Solutions at high-performing and secure ICT solutions provider, Datacentrix.

According to the 2020 Deloitte report, ‘Navigating the New Normal with Data-Driven Decision-Making’, the onset of the COVID-19 pandemic has had a profound effect on anything that could be considered as ‘normal’. The paper says “businesses that survive the initial crisis will have to navigate through the recovery period, which some analysts say will be even harder on balance sheets than the shutdown.

“Organisations need to perform complex analysis such as segmentation, eligibility, personalisation, and trend and options analysis when delivering customer and/or citizen services,” it continues. “A startling fact, however, is that to get through this time, organisations might not be able to rely on their well-established decision-making tools and models. Tools like artificial intelligence (AI), machine learning, and predictive models won’t be able to function exactly as designed in this new normal. Why? Because they will be based on historic precedence in a time where everything will be different in unprecedented ways.”

“The Deloitte report also states that if data-driven decision-making was critical for businesses to remain competitive pre-pandemic, it is now an absolute ‘tool for survival’, explains Jhazbhay.

Building an effective data strategy

“How, then, do companies take the plunge, and build an effective data strategy to help them transform in line with what is needed for today?” he asks.

“The first step an enterprise needs to take is to assess its current maturity. Once they understand where they’re at, in broader terms, the company will then have a better grasp of where to go from a data management point of view. This entails looking at what the business wants to achieve through the management and analysis of data, and putting together a definitive strategy based on its specific needs.”

After undergoing this evaluation process, a company might find that it doesn’t need a ‘bells-and-whistles’ data management strategy at all, as there is no business requirement for this, and capabilities like AI can be retained for business applications.

How can data be used for business advantage?

Once a data management exercise has been completed, a critical step within this process is to understand exactly what it is that the business expects to get out of a data management strategy.

The business assessment helps to pinpoint how a company should leverage data within different areas of the business. For instance, a more complete view of data can allow for a deeper understanding of buying patterns for a consumer-facing company, enabling more targeted upselling, better intelligence for management, and, ultimately, more revenue.

“Data tiering comes into play next, once a company has a clearer understanding of what the business’s data needs are, which data is critical, and which less so. For example, data received via sensors within the manufacturing plant of an automotive company is important, but should not be cross-pollinated with business data.”

And, maintains Jhazbhay, when developing data protection, governance and redundancy policies, certain principles must be taken into consideration.

These include the following:

• Data must be processed lawfully, fairly, and in a transparent manner (lawfulness, fairness and transparency);
• It must be collected only for specified, explicit and legitimate purposes (purpose limitation);
• It must be adequate, relevant and limited to what is necessary in relation to the purposes for which it is processed (data minimisation);
• It must be accurate and, where necessary, kept up-to-date (accuracy);
• It must not be kept in a form that permits the identification of data subjects for longer than is necessary for the purposes for which the data is processed (storage limitation); and
• It must be processed in a manner that ensures its security, using appropriate technical and organisational measures to protect against unauthorised or unlawful processing and against accidental loss, destruction or damage (security, integrity and confidentiality).

Becoming a ‘data’ company

Says Jhazbhay: “There are many technologies and systems that can bring value to an organisation, from business intelligence to predictive analytics. What is critical when it comes to powering these tools though is current, clean data.

“Every company is now a ‘data’ business, it really boils down to using its insights to improve decision-making and drive business value. Data is king, and it is here that companies will find the key towards navigating themselves successfully through the current crisis,” he concludes.