So many companies speak of having a data strategy, but what does that actually mean? Anyone can collect data, and anyone can analyse it. The trick is collecting the right data, and undertaking insightful analysis, so you can achieve valuable business benefits.
There are many tools for collecting and dissecting data, but the challenge is using them to derive useful insights or to answer an important question. And perhaps the most important question a business wants answered is: What does my customer want?
However, what if data analytics could answer the question before the customer even thinks it? This is the goal of today’s analytics platforms, powered by tools such as artificial intelligence (AI) and machine learning: Know what your customers want before they even have to ask.
The challenge doesn’t come from trying to write the algorithms — talented software developers can do this. The challenge comes from having enough real-time data to support the algorithms.
When it comes to building a great data analytics program, research indicates that access to the right real-time data is vital. If you think about your favourite sport, you realise that athletes don’t make in-game decisions based on history — they make decisions based on what’s in front of them in the moment. While old analytics programs relied on batch uploading of data at defined intervals, business actually moves faster than batch cycles. You need to design your platform to use real-time data.
Your marketable advantage comes from being able to react to real-time inputs faster than your competitor.
Therefore, a successful analytics platform to meet business needs focusses on getting access to real-time data in a usable format. This is turn lets you answer important questions to meet customer needs ahead of the competition. Sounds simple enough. But all this can happen only if you have the right leadership in place from day one, deliver value from the start, make small gains and ensure that you have the resources in place to continue development, so, ultimately, your data analytics platform will continue to evolve with your business.
To undertake a successful analytics program, you need to have a solid understanding of the problem and not just jump straight to a possible solution. Often, when faced with a technical challenge, the temptation is to jump into solution-mode. But getting an analytics project right isn’t about creating a database of guesses about what the customers’ needs might be — it’s about fundamentally better understanding the customer. A database may be the tool you choose, but it’s not the right place to start.
The success of an analytics project also hinges on governance. It’s critical to decide, from the start, whether the project is IT-led or business-led. And it’s critical to pick the right internal champion — someone with both with the authority and the responsibility to keep the project moving ahead.
It’s also important not to be over-ambitious when you start. Many projects lose momentum when you are trying to do too much, too fast. Getting analytics right can be challenging, so choose a problem to solve that is important to the business but has some neat boundaries. For example, rather than build an AI and analytics tool to manage all your inventory, perhaps focus on just one specific product line and build a tool to ensure you’re never over- or under-stocked. In this way, you will get some quick runs on the board that will provide demonstrable benefits to other areas of the business. Once you get one part right, only then should you expand it to other products.
Analytics programs are also not static, since businesses operate in a constantly changing environment. The actions of competitors, regulators, suppliers and customers are continually changing, which means that the questions you ask, the data you collect, and the responses that are crafted also need to continually evolve.
Plan for your analytics program to be an evolving development and ensure that there will be ongoing support for continuous development after the initial development budget. Make sure that the designed system can be augmented and changed so it will continue to meet changing business needs.
In this way, you will make the most of your analytics platform and data strategy.