The information age has provided businesses of every shape and size with extraordinary amounts of data. These data might concern customer preferences and behaviour, supply chains, finance, and myriad other areas of concern.
But if your business is going to act on the data that it obtains, then it will need a process for learning from that data. So, how do we turn raw data into actionable, business intelligence?
Understanding the Data Lifecycle
Data doesn’t enter the business in a state that’s fit for consumption. Trying to divine meaningful insights from raw data might be an exercise in frustration and futility. Instead, data will need to be processed, cleaned and presented so that it can be analysed and acted upon by key decision-makers. Along the way, it will also need to be stored.
For example, a dataset might quickly and efficiently be scrubbed of noise by using a rolling average. This might smooth out short-term fluctuations so that longer-term trends can be more easily identified.
Implementing Robust Data Governance
In order for the data to actually be useful and the organisation using it to stay on the right side of its legal responsibilities, it’s worth implementing policies and procedures that govern its use within the organisation. The framework that you devise should be regularly reviewed, and it should align with the regulations that govern the collection and storage of data. In the UK, this is the Data Protection Act 2018, which is an implementation of the EU’s General Data Protection Regulation.
Leveraging Advanced Analytics Tools
Getting the most from your data means being able to analyse it using sophisticated software and techniques. The larger the dataset, generally speaking, the more important it is that it is parsed in the appropriate way. Mishandle the data, and many of the valuable insights it contains could be destroyed – or completely upended.
Through the use of machine learning and artificial intelligence, many data analytics platforms can spot patterns that might have eluded more traditional methods. As such, this kind of approach might at least be considered – even if AI doesn’t represent a ‘magic bullet’ solution to data analytics problems.
Integrating Data Across Departments
The various parts of your business may collect data from various sources. This data might be useful to the business as a whole – but only if it’s shared. The phenomenon of data ‘siloing’ should, therefore, be anticipated, and processes should be put in place to deal with it.
Creating a unified ecosystem for data, in which departments can access the data they need – but only the data they need – could help you gain valuable insight while still remaining compliant with regulations.
Developing a Skilled Data Team
If you don’t have the right data analytics experts in place to help you deal with the data you collect, then you’re unlikely to make good use of that data. The team should ideally comprise a blend of data scientists, analysts, and engineers, whose combined expertise might help to guide the decisions you make in the future. As such, your strategy for dealing with data should be viewed as a recruitment problem, first and foremost.