In a time when data has been labelled 'the new oil', businesses are scrambling to implement effective forward-thinking data management strategies that can deliver real-time insights and business value to decision-makers to drive business strategy, increase revenue, and grow profits.

Data modellers have become indispensable assets to enterprises wishing to leverage their data to drive competitive advantage. However, there is often a disconnect between the data modellers analysing and extracting value from data, and the business decision-makers who need to utilise data as a strategic asset to drive business outcomes.

Closing the misreporting gap

Historically, businesses owned vast amounts of structured and unstructured data in their ERP, transactional, and other business systems, which was brought together in a data warehouse.

Here, a range of different data modelling tools, from the conceptual (showing relationships between different entities) to the logical (looking at certain attributes within the data) and physical (referring to how data is represented and stored using a database management system) were applied to create a framework within which analysts could extract business value.

However, different lines of business, each with their own ERP systems and internal processes, often created huge potential for misreporting as business analysts tried to combine disparate data sets.

In industries such as banking and retail, daily reports on the performance of the business had to be compiled from these data sets. To further complicate matters, a report may have arrived at a line manager's desk in Excel form, supplemented with additional information, and then delivered to the next more senior person in the reporting line.

Any errors would have a direct impact on the overall business' ability to navigate a challenging business environment or take advantage of an emerging opportunity.

Today, technologies such as IoT has led to escalating volumes of structured and unstructured data. Sensors on equipment and other business assets generate vast amount of data, putting strain on IT operations and analysts who are tasked with processing the data and extracting insights that can guide the business strategy.

According to the Harvard Business Review, on average less than half of an organisation's structured data is actively used in decision-making, with less than 1% of unstructured data used at all. Inefficiency is also rife: as much as 70% of analysts' time is spent on discovering and preparing data.

Offensive and defensive data strategies

Effective enterprise data management requires companies to assign data as either offensive (typically data that focuses on supporting business objectives such as increasing revenue and profitability, and ensuring customer satisfaction), and defensive (referring to data that helps minimise risk, such as ensuring compliance with regulations, maintaining the integrity of financial reports, and limiting fraud).

Offensive data activities require real-time analytics to deliver the requisite business insights and value to decision-makers. An offensive data strategy aims to generate customer and market insights, equipping decision-makers with critical insights through interactive dashboards.

Defensive data activities largely aim to produce a 'single source of truth' by ensuring the integrity of data flowing through the organisation.

Typically, defensive data management strategies aim for control by optimising data extraction, standardisation, storage and access; offensive data management strategies strive for flexibility by optimising data analytics, modelling, visualisation, transformation, and enrichment.

An effective offensive data management strategy requires the flexibility to produce multiple versions of the truth to suit the needs of different end-users by adding relevance and purpose to data sets: for example, weekly revenue figures that reflect the key insights required by multiple lines of business and can be adapted according to the needs of each.

This will prove critical in 2018: Forrester Research predicts that the majority of Chief Data Officers will move from defensive to offensive data strategies, with 50% reporting directly to the CEO (up from 34% in 2016 and 40% in 2017).

Machine learning, cloud enabling data management success

Two key technologies are driving the advancement of business analytics that support real-time decision-making in 2018: cloud, and machine learning. When organisations move their transactional environment or business systems into the cloud, it provides them with two critical benefits: lower running costs, and standardisation. In fact, Forrester Research predicts that half of all enterprises will adopt a cloud-first approach to big data analytics in 2018.

Due to the escalating volume of data, organisations are also increasingly turning to machine learning to automate data analytics. There is simply no way a business can afford to employ an army of analysts to sift through data to find value: a small team of expert data scientists supported by machine learning algorithms will enable organisations to leverage their data to achieve positive business outcomes.

Machine learning can also help rank the importance of data. Executives are equipped with real-time contextual information and ad hoc analysis by leveraging lines of business data from multiple sources to provide a single source of truth for the organisation. Since the analytics layer queries directly into the transactional or data warehouse, decision-makers get real-time information on actual business performance, with the flexibility of presenting the information in a variety of ways depending on individual needs.

The disconnect between business analysts and the business decision-makers they support has been reduced significantly thanks to the evolution of powerful cloud-based real-time analytics platforms. Business leaders in 2018 need to start the journey toward a cloud-based world of actionable business insights leveraging one of their most powerful - and often underused - assets: data.

* Neil Herbert, Director: Business Analytics at SAP Africa.