Data is the new oil…or gold…or black (or however the analogy goes).
That’s right - in this day and age, those with access to leverageable data wield a considerable amount of power – but only if they know how to use it.
Just look at this year’s Cambridge Analytica scandal, which unveiled the shady dealings of political campaigners in both the US and the UK that made use of illegally-obtained personal data from Facebook users to target voters.
Mimicking this deceptive approach will do you more harm than good - but there’s plenty of opportunity out there for organisations to utilise their own legally-attained data in a way that supports business decisions.
It seems that utilising data to make better business decisions is on the agenda for the majority of organisations, with almost three-quarters (74%) saying they want to be “data-driven,” according to a study by Forrester. However, data is only valuable if it transpires into meaningful actions - only 29% of organisations said their data efforts have led to actionable insights.
While there are many platforms out there that offer inbuilt analytics and insights, more often than not you’ll end up with a hefty amount of data – very little of which you can actually put to good use. To measure your data, analyse it and produce worthwhile, data-driven actions, you’ll need a team of experts to take the reins.
Generally speaking, there are three functions that fit under the data umbrella; collecting data, analysing it and producing actionable insights:
Collecting data: laying the groundwork.
When people think of data, more often than not, the sexier functions like artificial intelligence (AI) and machine learning tend to spring to mind. But what most people aren’t aware of is the amount of work that goes on behind the scenes to bring that data to life.
To effectively analyse and utilise anything, your organisation will need the right structural foundations in place to build a backlog of high-quality data.
By and large, there are two types of data: quantitative (measurements, such as numbers or text) and qualitative (observations, such as the colour of your eyes or your favourite football team). You’ll need an expert’s help to start collecting this raw and unprocessed data and determining what is and isn’t worth keeping.
A good example of a data collection role is a Data Engineer. Coming from a background in big data, Data Engineers are responsible for cleaning, aggregating and organising information - effectively creating an infrastructure that keeps the data flowing for other professionals to analyse later down the line.
Analytics: pre-empting patterns.
Next on the list is analytics experts. These specialists will help you process, aggregate and organise data – then translate this data into comprehensive visualisations such as reports and dashboards.
In theory, these analytics professionals will help you gain a better idea of how your organisation is performing over time, provide a better understanding of your current brand sentiment, and deliver behavioural patterns and trends within your industry and across the board.
For instance, a Data Analyst is someone who could utilise data to perform strategic analysis of your business’s performance, analyse the results of A/B testing, or take the lead on your organisation’s Google Analytics account.
Insights: determining your direction.
Data and analytics are only worthwhile when they bring about valuable insights, so having someone within your organisation that can translate these findings into meaningful actions is crucial.
With an eye for identifying areas for business growth, insights experts are able to challenge pre-existing actions or responses, and suggest more effective solutions going forward.
For instance, they could tell you the best time to go to market with a new product, or identify what stage of the sales funnel your potential customers are dropping off, or suggest practical ways to improve a team’s productivity.
A good example of an expert in this field is an Insight Analyst - someone who analyses reports and dashboards to look at historical trends in data sets and suggests innovative new solutions.