Most board-level executives know that their decisions are only as good as the quality and reliability of the data-based insights that inform them. But how good are they at spotting the factors that could be compromising data integrity?

Research completed by Vendigital earlier this year revealed that 79% of C-suite executives don’t always trust their business data. In a world where data awareness has improved significantly, relatively few senior-level executives claim to have access to a Single Source of Truth, but almost all understand the reasons they need one.

Among the tell-tale signs that an organisation’s data is not being well managed and could be misinforming decision makers is the lack of a clear data strategy that is aligned to the business strategy. In an age where workers at all levels are empowered to uncover and make use of their own data-based insights through the use of Power BI and other visualisation tools, disparate pools of business intelligence can arise which could undermine data integrity. In other cases, issues with data trust can arise if multiple, overlapping systems are pulling in datasets from across the organisation, to different timelines, which may not be aligned with the business’ strategy.

Issues with data trust are common in post-deal situations, where, for example, a parent company might have much better data management and governance in place than the business it has acquired. In these situations, the solution might be to create a new MI (management information) suite of harmonised data, which is aligned to the data strategy of the merged organisation. Even once this is achieved, if one business has a culture of making last-minute amends, and resubmitting data that fails to find its way into reports produced by the new harmonised model, this could undermine trust all over again.

Some signs that business data could be misleading internal decision makers include:

A ‘last-minute’ culture for reporting

Every business needs a single source of truth to guide their strategic decision making and to ensure they are delivering against business’ objectives. A culture that allows teams to make last-minute changes and run over pre-agreed deadlines for reporting will inevitably compromise data trust.

Gaps in Master Data Management systems

Master Data Management systems tend to be the most valuable and informative when it comes to guiding strategic decisions, but what happens if core system data, from an ERP system, for example, has gaps? Product codes or quantities might be missing altogether or data could be entered incorrectly, meaning that data could end up being zero lined which in turn contributes to mistrust in the resulting MI that references it.

Critical business data is being misused

For some businesses, data acquisition may be critical to business performance. However, the processes involved must be carefully controlled. For example, CRM data may be required by the sales team, but if fields are incomplete, or disparate reporting methods are used, the data could prove misleading.

Lack of process for validating data

If data errors or gaps are evident, these should be flagged and either blocked or corrected, before they end up in a report. Running a validation test prior to including the data will help to identify potential anomalies. For example, the accidental insertion of too many zeros or irregular use of commas or colons at the point of data entry can both skew outputs significantly, and cause huge delays in data processing time.

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Poor adherence to governance best practice

Having a data strategy in place is one thing, but the business must also adhere to data governance best practices. If not, this could breed a culture of data mistrust. A rigid adherence to good governance is needed in all areas of data acquisition and management to ensure the right information reaches the right people in the right timeframe, at the right quality.

When embarking on a data transformation project, consultants may find that the business’ data is good quality, but is being applied incorrectly or there is a lack of interrogation. Therefore, certain questions should always be asked. For example, is it aligned to the business’ strategy and delivering the right insights? And are internal data teams asking the right questions of their data? This is an area where almost all businesses will find some room for improvement.

Increasingly, businesses are looking to gain a competitive edge and drive enterprise value through data management and business intelligence. This could be achieved by using data to personalise customer experience or to tailor a product or service to meet a specific set of customer requirements. AI-based systems can help to increase demand cycle understanding, enabling businesses to manage inventory levels and produce more reliable demand forecasts. For some industries, such systems can be used to apply predictive maintenance; ensuring that the life expectancy of key assets is optimised while improving operational efficiency.

By using the data opportunity to develop valuable business intelligence, businesses can become better at what they do. Implementing a data strategy that is aligned to the business’ strategy is a critical starting point, but there is little point in doing this unless the business is serious about protecting data integrity at every turn.

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