As the world becomes smarter and smarter, data becomes the key to competitive advantage, meaning a company’s ability to compete will increasingly be driven by how well it can leverage data, apply analytics and implement new technologies. In fact, according to the International Institute for Analytics, by 2020, businesses using data will see $430 billion in productivity benefits over competitors who are not using data.
So, it’s clear that data is now a key business asset, and it’s revolutionising the way companies operate, across most sectors and industries. In effect, every business, regardless of size, now needs to be a data business. And if every business is a data business, every business therefore needs a robust data strategy.
It all starts with strategy
Having a clear data strategy is absolutely vital when you consider the sheer volume of data that is available these days. I see too many businesses get so caught up in the Big Data buzz that they collect as much data as possible, without really considering what they want to do with all that data. While others are so overwhelmed by options that they bury their head in the sand. Neither represent a smart way to run a business.
Instead of starting with the data itself, every business should start with strategy. At this stage, it doesn’t matter what data is out there, what data you’re already collecting, what data your competitors are collecting, or what new forms of data are becoming available. Neither does it matter whether your business has mountains of analysis-ready data at your disposal, or next to none. A good data strategy is not about what data is readily or potentially available – it’s about what your business wants to achieve, and how data can help you get there.
Therefore, if companies want to avoid drowning in data, they need to develop a smart strategy that focuses on the data they really need to achieve their goals. To be truly useful in a business sense, data must address a specific business need, help the organisation reach its strategic goals, and generate real value. This means you need to define the key challenges and business-critical questions that need answering, and then collect and analyse the data that will help you address them.
I see a lot of companies with data strategies nestled within different areas of the business, such as marketing and sales. That’s not enough. Every business needs a company-wide data plan. Unfortunately, there is also still a widespread perception among business executives that data and analytics is purely an IT matter. And as with all IT matters, this means they don’t really need to understand how it works. They simply need to know what it does – drive growth – and throw money at it. In my experience, data strategies that are driven by the IT team tend to focus on data storage, ownership and integrity rather than the business’s long-term strategic goals and how data can help reach those goals. That’s why the data strategy should be owned by the leadership team.
It is also important to remember that no one type of data is inherently better than any other kind. Using data strategically is about finding the best data for your company, and that may be very different to what’s best for another company. With so much data available these days, the trick is to focus on finding the exact, specific pieces of data that will best benefit your organisation.
The key elements of a good data and analytics strategy
To create a robust data and analytics strategy, business leaders need to consider many factors. Here are the critical points I would expect to see in a strong data strategy:
- Your data needs – In order to find the right data for you, you must first define how you want to use data. You may need certain types of data for some goals and different types of data for others.
- How you will source and gather the data – Having identified what you are looking to achieve with data, you can now start to think about sourcing and collecting the best data to meet those needs. There are many ways to source and collect data, including accessing or purchasing external data, using internal data and putting in place new collection methods.
- How that data will be turned into insights – As part of any solid data strategy, you need to plan how you will apply analytics to your data to extract business-critical insights that can inform decision making, improve operations and generate value.
- Technology infrastructure requirements – Having decided how you want to use data, what kind of data is best for you, and how you might want to analyse that data, the next step in creating a robust data strategy is considering the technology and infrastructure implications of those decisions. Specifically, this means deciding on the software or hardware that will take your data and turn it into insights.
- Data competencies within the organisation – In order to get the most out of data it is essential to cultivate certain skills. There are two main routes to developing data-related competencies in your organisation: boosting your in-house talent, and outsourcing the data analysis.
- Data governance – Collecting and storing data, especially personal data, brings serious legal and regulatory obligations. Therefore, it is vital any organisation factor data ownership, privacy and security issues into their data strategy. Ignoring these issues, or failing to properly address them, could see data go from a huge asset to a huge liability.
In business, information is power, and Big Data is providing information we couldn’t have dreamed of collecting or analysing just a few short years ago. With the massive growth in Big Data, plus the rapidly evolving methods for analysing data, the importance of data across every aspect of business will only increase. Those companies that view data as a strategic asset and develop robust data and analytics strategies are the ones that will succeed in this new data-driven world.
Where to go from here.
I have written a number of relevant articles with useful resources to help you develop your data strategy.
Here are great links to get you started: