The need for accurate data before adopting new systems and business processes

Big data . . . YES

Adoption of cloud-based systems . . . YES

The need for accurate data to make business decisions . . . YES

We all recognise the need for good data, but where we all have vast amounts of data, how much of it is being used for decisions in your business and how much do you believe is reliable?

Our business applications are designed to be able to store infinite amounts of data, meet compliance needs and run reports that are essential to the business. All of which provides significant business benefits to any organisation. These tools are becoming fundamental but are only as good as the data you have in the system.

What if there is one digit in a reference code that means a report doesn’t include that claim, what happens if there is a typo in a field that means that when you run the report, it doesn’t get picked up?

The quality of unstructured and semi structured data is vital in the adoption of new systems to be able to provide you with the ability to interrogate your information and rely on the intelligence produced.

The initial (big project) data quality step

There should be a measure of the quality of your data based on factors such as:

Accuracy – is it structured correctly and are the right values being used? Does it conform to the right formats across the organisation?
Completeness – does each data set contain all the data elements it should?
Consistency – making sure there is no conflicting data between the same values in different data sets.
Reliability – can you comfortably say there are no duplicate records?

Assessing the quality will help identify data errors requiring resolution and get the data to a point of ‘fit for purpose’. Taking this first step to cleanse / scrub your data will provide confidence in your next steps. A start to no more inaccurate analytics and ill-conceived business strategies and a move to a greater trust in data for business intelligence, improving the decision-making process and internal processes which in turn can help with improving bottom line profits and provide a competitive edge over rivals.

The battle for accurate data is time

Once the errors are identified, corrections can be made and internal communications raised to notify everyone of the improvements. Processes can be put in place for e.g. monthly audits or an additional stage in a project for cleansing, so that your organisation can continue to have faith in its business intelligence.

However, cleansing data is a timely task to manage in between the day-to-day activities. Or is it?

This is where technology (process, speed, affordable), when combined with people (knowledgeable problem-solving specialists), can work well; and provide results in minutes rather than days, weeks or even months. Then, when you calculate on the ROI of working with clean and accurate data to make business decisions; the cost of using a service provider to do this, is an ideal solution.

Data Integrity as a Service (DIaaS)

If a service provider has the technology, along with the experience and knowledge of the market, could you outsource this task to them? Could this be a reasonable solution to your frustrations and blockers to some of your projects?

We’d like the opportunity to understand more about your projects and explain more specifically about how our technology/people solution can provide you with the results you need in extremely efficient timescales. Ultimately, you provide us with the data, tell us your concerns and we provide you with the cleansed data in a usable format for your business and a report about what has been identified.

If you would like to understand more about our data integrity service, contact our team on 020 7971 1141 or email data@legacydatasolutions.co.uk