a data-driven business

Your Tough Data Challenges and how to Tackle Them

Data drives our world. The word “data” is almost always associated with reports, decision-making procedures, and grand strategies for gaining a competitive edge. Nevertheless, most data specialists struggle to define data, let alone explain what real benefits it can bring a company.

To help identify the pitfalls of data and harness this often-undervalued resource to its fullest extent, Robinson Ryan has compiled this list of six tough data challenges that apply to almost all major organisations.

Staff saving local copies of corporate data

No matter how prudent you are, there is always a chance that locally saved information could be changed, corrupted, or deleted. It can even take the place of the official data of record. As a result, saving data safely, and sharing only a mirror copy with employees is critical. After all, if incomplete or unauthoritative data is used for business analysis, lost revenue, opportunities, and reputation may result.

Data Challenges in System Upgrades

It may seem basic but making several back-ups of data and upgrading systems through legitimate sources is imperative. Each time a data management system or piece of hardware is updated, information can – and does – get lost or corrupted.

Accessibility For Teams

How data is shared and made available to all employees is the cornerstone of any effective system for sharing corporate data. If not done effectively, the information used by data scientists to evaluate, theorise, and predict often gets lost. This obstructs the way data trickles down from business analysts in large corporations – from departments, sub-divisions, branches, and finally to the operational teams – and can lead to incomplete information being passed on to the next user.

Inconsistent Formats

In 1999, NASA lost its $125-million Mars Climate Orbiter because its engineers failed to convert from English to metric measurements. Said otherwise, a simple mathematical error stemming from inconsistent formats was to blame.

The same goes for data. If data is stored in inconsistent formats, the systems used to analyse or store the information may not interpret it accurately. As an example, simple things such as name, date of birth or phone numbers should be saved in the same format across the board.

Incomplete Data

It happens all too easily. Often data can be entered into a system incorrectly, certain files may be corrupted, or the remaining data has several missing variables. An example of this is if an address omits the postcode. This renders the information defunct as the geographical aspect cannot be determined.

Duplicate Data Challenges

Duplicates overburden computation and storage space. Worse, they can produce skewed or incorrect insights, particularly when they remain undetected. Typically, human error is to blame. However, it can indicate a malfunctioning algorithm.

One solution is using human insight, data processing and algorithms to flag potential duplicates based on likelihood scores and common sense where records look like a close match.

If tough data challenges are getting between you and leverage for your business, contact Robinson Ryan today to set up a consultation and let’s discuss potential solutions.

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