Without a clear strategy it is difficult to determine priorities and where to focus attention. Strategies are even more important in areas that are complex or fuzzy and without a plan of how and where to approach these areas people can easily get lost.
Data is one of these areas where I see so many companies get lost. We now live in a world in which the volumes of data are exploding by the second. While some companies are leveraging these data assets very well to generate mouth-watering competitive advantages, most are completely overwhelmed by the amounts of data they are generating themselves, let alone all the other, external data they now have access to.
I believe that those companies that can turn data into valuable insights are the ones that will thrive. The ones that continue to only dip their toes in the water of data and analytics will be left behind. And those that ignore data altogether will wither away.
Every company, big or small, in any industry, needs a data strategy – a plan of how to use and leverage the vast amounts of data we can now use. The buzzword ‘Big Data’ is used to describe the datafication – for those who would like a brief overview, here are some slides:
In practice, I see that most companies and executive teams are scared or overwhelmed to even start with a data or ‘big data’ strategy. I always try to make it clear that by following some very simple steps anyone can start with strategic data thinking.
Here are my 5 steps to a data (or big data) strategy:
1. Start with your strategy and information needs
When I help companies with their big data strategy I make sure we don’t start with all the data you might have or could have access to, I start with the company strategy. Think about the strategic priorities you have laid out for the coming months or years. Define what it is you want to achieve and have to focus on to deliver that. Then think about the big unanswered questions you have about delivering your strategy. This is how Google now run their business – buy defining and answering their key business questions. Defining the questions will help you identify the information needs and by making sure they are linked to your strategy you make sure they are the most important and strategic information needs, rather then every little minute question that would be good to know.
2. Define the data to answer your most critical questions
Most companies get so caught up on collecting data on everything that walk and moves, because they can, rather than collecting the data that really matters. This might sound paradoxical but when it comes to big data is it even more important to think small. I have recently worked with one of the world’s largest retailers and after my session with the leadership group their CEO went to see his data team and told them to stop building the biggest database in the world and instead create the smallest database that helps the company to answer their biggest questions. This is a great way of looking at data.
Look at each question and then think about the ideal data you would want to answer that question. Once you have defined the ideal data set, look inside the organisation to see what data you already have. Then look outside and establish what data you could have access to. At this point you can then decide whether to use existing internal data, bring in existing external data or create new data collection mechanisms.
3. Establish the analytics needs
Once you are clear about the information needs and the data, you need to define the analytics requirements, i.e. how you will turn the data into insights. Here you define how the data will be analysed to ensure the raw data is turned into insights and value.
4. Establish the reporting and access needs
In this step you define how the insights will be communicated to the information consumer or decision maker. Most companies report ‘raw’ data in ways that makes it very hard for anyone to understand the meaning and messages that are contained within the data. You need to think about the format, visualisation of data, and the interactivity. E.g. will the data be presented in reports, advanced visualisations, interactive self-service dashboards or infographics? Always keep in mind that we want to find the easiest way to present that data so that it helps people answer their critical questions.
5. Define the software and hardware requirements
Following on from defining what data is needed, how it will be turned into value, and communicated to the end user, it is time to look at software and hardware requirements. Is the current data storage technology right? Should it be supplemented with cloud solutions? Is the current analytics and reporting technology right? Etc.
6. Define action plan, including training needs
Finally, it is important to define an action plan to turn the data strategy into reality, including milestones, who is doing what by when as well as any training and developments needs.
I have used this approach with companies and government organisations of any size, across many sectors, and find it a simple and intuitive approach.
What do you think? Any comments? Have you got a data strategy in your company? Or are you among the many where this is missing? Please share your views…