If data is the new life-blood of business, you must ensure it's efficient, relevant and drives competitive advantage.
For some, the data landscapes of their businesses have become just too large to see the wood for the trees. As a business grows, inevitably, so will the amount of data they hold. Meaning it runs the risk of being too complex to grasp – let alone optimise. Expanding data volumes and a growing number of systems that collect, process and analyse it make data quality a complex task. No company can afford to bury their heads in the sand about the issue. The reliability of corporate decisions depends on the relevance of your underlying data.
Not only this, there is the chance that valuable knowledge will be locked in the minds of individual teams, meaning a business can’t act with agility or even have confidence in it to make significant business decisions.
So how do you proceed in making your data the most useful tool for business improvement? Ideally, a data strategy will already be in place, before vast amounts of information begin accumulating. However, there are some general steps you can take to creating a clear plan.
Getting Your House in Order
The first step to putting your data to work is to have a clear and concise strategy to clean your data, ensuring its quality. Remember, ‘quality’ can be defined in different terms, depending on what is required from it and even differ from department to department – meaning quality expectations vary considerably. Having a structured data cleansing plan will create more transparency around an organisations’ data, such as where it came from, how often or whether at all it is needed and who needs access to it.
Next, it is necessary to set some clear goals and carry out an audit to assess how close the business is to them. In other words, you need to know how this data will be used and what purpose it is going to serve. For example, the data that is needed to increase sales will greatly differ from that which is needed for decreasing fraud or offering personalised services to customers.
As you are working through this, pay attention to where there are any shortcomings. Are there any problems that occur repeatedly? Known bugs within certain datasets? Is there data that could be deleted completely? Doing this will allow you to see which data holds the most important and where the greatest positive results can be expected.
Don’t Set the Bar too High
In fixing these goals, bear in mind that setting the bar too high can be detrimental. It’ll be much more beneficial to build on established best practices from comparable companies and industry standards – rather than reinventing the wheel. There are, of course, compliance requirements that will need to be taken into consideration too. Also, data quality is often an interface issue, and just as much care should be taken to ensure that is it able to be seamlessly shared between systems and businesses as well as with suppliers and customers.
Data cleansing and goals are just the beginning. Once your ideal standard has been reached, to make sure that your data maintains its business value, the next goal is to maintain its quality over the long-term. Your business will continue to generate new data; there is no escaping that. What is needed next is an automatic monitoring tool that can quickly detect any flaws or anomalies in datasets. Such solutions can uncover and correct any issues before they impact your business, by pinpointing where the error has come from and most importantly, formulate preventive quality guidelines.
Such a system can also serve as the central location for all data rules in the company. This will make information permanently measurable, ensuring you can review if it is still aligned to your business goals as and when needed.
Data is the life-blood of every business, and following these steps, you can ensure yours is efficient, relevant and drives competitive advantage. It shouldn’t be overlooked that data is also the element that connects processes, people and technologies in the digital age. All three should be part of the long-term goals for both data improvement and business growth.
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