Harnessing the Power of Data to Drive Business Growth

Every organisation has a not-so-secret weapon: data. Incredibly valuable information generated from every aspect of the enterprise. From research and development to the supply chain, sales performance, human resources, marketing, operations as well as customer service and behavioural insights. Every activity in every facet of the business provides more data. Periphery data outside of businesses’ control, such as social media, syndicated or other public data, is critical, too. 

For organisations planning to stick around, harnessing the power of data is a business imperative because of the opportunity it holds: competitive differentiation. When armed with the knowledge and capabilities to digest data and extrapolate meaningful insights, organisations are poised to make informed and strategic decisions about all areas of the business. In turn, these data-driven decisions can help save time and money, create new revenue streams and exceed customer expectations. 

Unfortunately, many organisations struggle with using data to their advantage. The reasons include: 

Over customised technology: Legacy ERP systems can be overengineered to fit an enterprise’s precise needs. This level of customisation becomes burdensome to maintain and scale over time. 

Costly new technology: Replacing the old with the new, on-premise implementations can be a massive investment. 

Misguided technology selection: Best-in-class enterprise applications and systems demand the integration of multiple technologies; mistakes can be made in this important process, hampering connections. On the other hand, by going only with standardized platforms, organizations may sacrifice desired features and tools. 

Lack of talent: Organisations may be eager to hire a data scientist to help them navigate their data. Research recently conducted by TEKsystems found big data to be among the top five most sought after (yet hard to come by) technology skills in the current talent landscape. 

Managing expectations: If they do want to hire a data expert, organisations need to recognise the shortage of professionals with these niche skills and the high cost of employing them. Most organisations admit they need an innovator who can deliver transformational analytics solutions; many do not allocate the budget required to attract that caliber of talent. 

Success Starts with a Data Strategy 

A data strategy helps organisations map the business objectives, needs and priorities they’re trying to solve with their data. It ensures future technology decisions and business activities are sound and aligned with enterprise goals. Establishing a data strategy – and reviewing it regularly – not only paves the way to informed decision-making and successful business outcomes, but it also positions the organisation to competitively thrive. 

In fact, research proves that productivity and profitability improve when an organisation is empowered by data. Organisations with mature data strategies boost profitability by an average of 12.5% of gross profit. And organisations that incorporate big data and analytics into their operations realise productivity and profitability rates that are as high as 6% greater than the competition. Cost and productivity efficiencies directly help the bottom line. Data puts them one step ahead and ready to apply technologies such as AI and machine learning. 

Challenges Faced by Organisations Trying to Get the Most Out of Their Data 

Some of the most common challenges are: 

Too difficult to access data: In many cases, the governance models and policies that organisations employ to keep their data secure and their environments performant restrict access to the people who are in the best position to use the insights derived from it to improve decision-making and business outcomes. 

Data silos and inconsistencies: Because it is too difficult to access data – and/or the “single source of truth” – data silos often emerge and operate outside of the purview of IT. This not only leads to data inconsistencies across silos, but also creates inefficiencies across the organisation due to data replication, unnecessary movement of data and a greater risk of data misuse/abuse. 

Data literacy gaps: Even when business users get access to the data they need, they often lack the skills and overall data literacy to prepare and analyse data to find and validate insights. In many organisations, the act of preparing and analysing data is left to a small group of experts or power users, which limits scale and overall organisational impact. 

Culture change is difficult: Many organisations talk about their desire to be data-driven and rely on analytic processes to drive their decision-making, but few organisations have achieved this vision across the enterprise. A significant, and often difficult, culture shift is required to move from making decisions based on instinct to relying on insights derived from data analysis. 

Future-Proofing Data Technology Investments 

There are five core elements of future-proofing any technology investment, including those in the data and analytics space. 

1. Organisations should make technology investments that make them more agile with the ability to develop and deploy quickly and react to market and competition. 

2. Technology investments should also offer elasticity so that organisations right-size their infrastructure and save costs while serving market needs whenever required. 

3.  Organisations should also think whether their data platform could enhance their ability to innovate. Does it lower the cost of experimentation? Does it democratise experimentation or concentrate it in the hands of a few highly trained people? 

4. With organisations doing business across the globe, they also need a data platform that can be deployed easily, securely and in compliance with regions across the world. 

5. Finally, a large part of future-proofing an investment is predicting costs into the future. Escalating costs are a major issue with many organisations when they get locked into their technology choices. They need to look at pricing patterns over the last several years to decide if their costs will drop or increase in the future because of their data technology decisions.