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 and are simply scratching the surface of the other, external data they could use.
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 and are simply scratching the surface of the other, external data they could use.
Those companies that can turn big data into valuable insights are the ones that will thrive. The ones that continue to only dip their toes in the water of big data and analytics will be left behind. And those that ignore big data will wither away.
Every company, big or small, in any industry, needs a solid big data strategy. While most are scared or overwhelmed to even start, I always try to make it clear that by following some very simple steps anyone can start with strategic big data thinking.
Here are my 5 steps to a 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 they 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 – by 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 ensure they are the most important and strategic information needs, rather then every little minute question that would be good to answer.
2. Define the data to answer your questions
Most companies get so caught up in collecting data on everything that walks and moves, simply 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 most important questions. This is a great way of looking at big data.
Look at each question and then think about the ideal data you would want or need 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 you can use existing internal data, bring in existing external data or create new data collection mechanisms.
3. Establish the analytics requirements
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. You need to think about the format, visualisation of data, and the interactivity. E.g. will the data be presented in e.g. reports, advanced visualisations, interactive self service dashboards or infographics?
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 and
Finally, it is important to define an action plan to turn the big data strategy into reality, including 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 to a big data strategy, and one that engages the key decison makers.
Any comments? Please let me know your thoughts…
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Finally, please check out my other posts in The Big Data Guru column and feel free to connect with me via Twitter, LinkedIn, Facebook and The Advanced Performance Institute.
