We all know how it feels to be excited about the potential of something, only to have it turn out less awesome than anticipated. Unfortunately, this is a common problem when organisations jump into analytics, and the people who were initially the most excited about the prospect often end up being the ones who feel most responsible when it doesn’t deliver value.
In the midst of the current hype and momentum around data analytics, there is a gap between that momentum and the corresponding value output (or lack thereof). Here are the four primary reasons I believe this happens, along with Datamine's suggestions for how to realise the value of analytics and scale efficiently while avoiding heartbreak and hassle:
1. There’s a disconnect between the business and technical areas
In a given organisation, there will be a business side of things and a technical side of things. Often these two business units are not as in tune with one another as they need to be in order to get value from analytics. It’s important for there to be accountability and communication between the two entities, as described below:
2. Analytics roles and responsibilities across departments are unclear
One common struggle companies face is the question of how to organise the team, or teams of data analysts. Businesses often hire techies but don’t have the structure in place to support them, which often leads to one of two things:
3. Projects are being completed, but the benefits aren’t being realised
The third major issue we often see preventing businesses from truly getting value from analytics has to do with setting up a framework in which it’s easy to realise benefits. One component of this is to make sure you’re first focusing on quick, repeatable projects that are easy to measure - being able to prove these projects work will give you the momentum and buy-in from management to continue with tougher challenges. It’s also important to have a prioritised pipeline of all analytics work that is visible both within the centre of excellence and across the business verticals. Without this, you’ll have different pipelines that are working independently rather than together, making it very difficult to tie value back to.
From there, you need to make sure you have the right technical setup, one in which you have three distinct areas:
Many businesses only have one area in which they do analytics and one ‘speed’ at which they try to tackle all these steps, which means they end up jumping too quickly from step to another and don’t get what they want out the other end. Separating your work streams into these distinct buckets can help you unlock the value at the end of the project.
4. Data is stored in the wrong platform and isn’t easily accessible
Most businesses have heard talk of data warehouses, data lakes and datamarts, but we’re seeing a lot of confusion around the different types of repositories and what they’re best used for. Here’s a quick rundown:
Businesses often unknowingly apply data warehousing principles to data lakes, or to datamarts, meaning they waste resource and hundreds of people hours trying to get value from the wrong kind of repository for the task they’re trying to do. Our suggestion is first to consider what you’re trying to achieve, then creating a datamart that serves that unique purpose - then you can pull data from the warehouse and lake (but only the data you need) and store it in the datamart. This avoids big headaches down the road, and the ultimate cost savings can be in the millions.
Chances are if you’re already trying and failing to get value from analytics, you’re probably making an effort to grow as a business rather than stay at the same level you’re currently at.
If that’s you, good news: the four areas described above aren’t designed for stability, they’re designed for acceleration and analytical growth. In order to stay competitive, businesses need to build and scale their analytics capability, and it can be challenging to do this alone – but people are often scared they’ll become hostages to a third-party supplier that holds and controls their data.
Rather than hold the power and do everything for you, Datamine is about helping you accelerate and build your internal capability through close collaboration with our diverse team of consultants, data scientists, visualisation experts and developers. Check out our case studies to read how we’ve helped companies in your industry get value from their analytics initiatives and regain competitive advantage.
Matt is a passionate advocate for using data-driven intelligence to identify and address business challenges. A big supporter of implementing analytics in Marketing, Matt has the expertise to balance the technical, commercial and cultural considerations required to derive value from analytics.