Seven features of 'good', reliable data
Data, the vital business asset
Most enterprises understand the importance of data in making decisions at the different stages of running a business.
Before launching, they run a market research or test an MVP (Minimum Viable Product) to ensure that what they plan to offer has demand.
When building a website, A/B testing is done to decide on the best copywrite and content based on the results from user interactions.
While running the operations, customer satisfaction metrics, financial data and many others are monitored to ensure that businesses are on track.
When it is finally time to scale, they look at the data again to confirm if the new plans have the potential to work out.
The internet bombards us with humongous amounts of information on a daily basis (data created on the internet is projected to grow to more than 180 zettabytes by 2025). We further top it up with our own data that is specific to our business, such as our clients’ information, their orders and feedback, our suppliers’ information, their performance metrics, etc.
This data, however, should not be haphazardly collected and stored. There needs to be a strategy and a framework in place to ensure that the data is managed properly in order to reap the underlying benefits. The question then is: how do we know that we are taking the proper steps to cater for our data?
There are indicators and characteristics that you can search for in your data to answer the above question.
What does 'good' data look like?
Here’s a list of the characteristics of ‘good’ data, which is data that can be used reliably in the business decision making process.
1) Has a purpose – Data has to serve the business vision
The importance of your data quality stems from the fact that it has a direct impact on your strategic decision making. The ultimate goal of collecting and managing data is to use it in meaningful business decisions and actions. Data must be focused on what matters most to your enterprise, therefore, consciously planning your data management activities and tying them to the business objectives and overall strategy is vital.
2) Available – Data has to be in the right designated repository
Data required for business operations and management decisions must be stored in the right designated corporate database or information management system. Another aspect to consider is the availability of these systems (and data) during operational emergencies and critical times.
3) Timely – Data has to be available in the timeframe in which it is needed
The timeliness of data is very important and becomes even more important in fast-paced (e.g. stock trading) and hazardous environments (e.g. Oil & Gas) where decisions impact lives, reputation and major expensive assets. If the data is availed after an incident occurs then it is obviously too late to use it to rectify or prevent that incident.
4) Accessible – Data has to be accessible by the people who need it
There’s no use of available and timely data if it’s not accessible by the people who are going to use it. Many applications and information management systems allow you to assign roles and access levels to different people based on what they need to do with the data. A clear demarcation of roles and responsibilities is essential in this case.
5) Accurate – Data must represent the reality
Data accuracy is an essential component of credibility, which is crucial for making good decisions and holding people accountable for those decisions. Some data may even trigger a complete pivot in the way enterprises run their business which makes data accuracy mandatory.
6) Comprehensive – Data must cover all business related aspects
There are many vital areas to be monitored while running a business, including – but not limited to – marketing, HR, operations and others. Each area has its own set of data that is needed to operate correctly and all the areas collectively participate in the success of the business.
7) Clean – Data must be quality checked
Data cleansing is among the most significant processes when it comes to ensuring your data is good (consistent formats, completeness of data fields, exclusion of dummy data) and valuable. Clean data is not necessarily accurate but is crucial for running reports that help visualize insights to facilitate the decision making process.
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