Some Tips For Effective Data Management

Augmented Insights effective data management

Data and its effective management is increasingly the power behind decisions made by organisations. Being able to base future changes to how you provide services to customers, improving their delivery and personal wellbeing, on evidence derived from business information provides certainty of outcome. And it is this confidence in achieving excellent results which empowers your colleagues to become more effective and professional in their work. So let’s look at some tips for effective data management that will help you improve how your organisation works.

Know Your Data Sources

A typical organisation will create and store data everywhere. Most will be able to quickly list whatever databases they operate. But other sources, such as laptops or handheld devices, can be forgotten. Then there’s hard copy data recorded in notepads, forms sitting in cabinets, even Post-It Notes curled up round the back of a monitor. And nearly everyone forgets the biggest data source – their people and all that tacit data held in their heads.

The non-digital sources should never be simply ignored as they may be vital in conjunction with other information to your data analytics plan. Instead, take the time to consider how this unstructured data can be digitised for storage and use.

Stick To Your Goals

All data has a value, no matter how apparently unrelated to the organisation’s operations. But not all data is needed. It might be great to have your data able to interoperate seamlessly, but that doesn’t mean it needs to be used. Understanding what you are trying to achieve in your data management strategy defines what information is important and what can be kept in the background.

Quality, Not Quantity

While some organisations fret about the exponential amount of data they hold, there are some who worry they don’t seem to produce very much at all. Fundamentally, the quality of the data you are using is far more important than the number of sources or sheer volume. This means you should look to set and reach standards around the following criteria:

  • Completeness. Data should contain all relevant information. Incomplete data introduces uncertainty in analytics.
  • Precision. Ensuring data resides and relates to their fields is essential.
  • Correctness. Data should conform to any validity rules in place.
  • Availability. Access to data for analysis and reporting should not be restricted

The Owner Must Be Responsible

Data can often reside in silos under different teams. It’s very common for functions to be aware they hold data but not understand the implications of this point and the connection they have to how the business looks for new directions. To this end, whoever manages those functions should understand the level of quality of that data is under their remit. It is their staff who provide the data and so they must ensure it is done correctly and regularly. If not, they must then understand poor quality data can consume time and resources to improve or run the risk of corrupting reports used by senior staff.

There is also a wider point to ownership &responsibility. An organisation is deemed to be the owner of any data it produces. Which means it has a governance role which, where such data includes personal information, is subject to legal compliance.  This brings in to play processes covering access, security, sharing, retention and destruction.

Be Proactive, Not Reactive

Managing data is far easier if it is produced and stored correctly from the outset. Looking ahead, it is also far easier if duplicate information is replaced by sharing data across silos, removing the need for staff to repeatedly provide the same data.

The same approach also applies to ensuring data is standardised. Different people may have different understandings of the same information, and from there create or store if differently. The knock-on effect is time and resource spent unravelling these differences to allow data to be useable. Instead, making sure people understand clearly what data definitions are in place gives them clarity and guidance, ultimately making their tasks in providing information simpler and faster to undertake.

Data Management Never Ends

Producing and maintaining data is an ongoing process. Regular and ongoing management is key to keeping your analytics processes in good health and supporting your organisation.

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