How is it possible that two people can view the same data so differently? Well, even in the big data era, with smart algorithms and great visualizations, getting the right answers is very difficult. Sometimes big data can make decision-making even more difficult and that has to do with not asking the right questions.
Everyone talks about asking the right questions, but what are the questions you can ask depending on the choices you have made? Each strategy or each use case requires different questions to be asked. In addition, the analyses done and the tools used also result in different important questions that need to be asked before, during and after developing and implementing a big data strategy.
There are many different questions that can be asked when developing a big data strategy and which question to ask depends on what you want to achieve with your organization and what you want to derive from your data. There are an infinite amount of questions that can be asked regarding data, all depending on the type of data, the source of the data, the volume, variety and veracity of the data, but also the type of organisation and the use case that you want to develop.
However, when developing a big data strategy, the amount of questions is a lot smaller. So let’s take a look at those questions. Some examples of questions can be:
- How does big data fit in our culture?
- How much money do we want to spend on big data?
- How much time do we have to implement a big data project?
- Which departments should be involved?
Of course these are almost general questions for any project within an organisation. Nevertheless they are important to ask, and answer, when developing a big data strategy. But there are also some more specific questions to ask. Let’s take a look at three of such questions:
Can we use the cloud or do we want to build on premises?
Deciding on where to store your data involves taking into account many different variables. The most secure location for your data is of course on premises in a private database. The least secure is a public cloud and in between there is the private cloud. Each choice has its own advantages and disadvantages.
Big data in the cloud will allow you to relatively easily boost the scalability, performance, manageability and reliability of a platform. In addition, many enterprise applications, especially for Small or Medium sized Enterprises, are already hosted in the cloud and leveraging that might be wiser than hosting it all in-house. Setting-up a cloud based big data system can be done faster than an on-premises system that also requires a lot of hardware to be installed. On the other hand, an on-premises big data system provides the organisation with control over their data, it is more secure and for large organisations generating vast amounts of data it can also be cheaper than in the cloud. Scalability on the other hand tends to be more difficult and it requires hiring sufficient big data employees who can operate the Hadoop clusters.
What data do we capture or want to capture?
A lot of organisations only think of the ‘standard’ data sources when developing a big data strategy. These sources can range from sales data, website data or inventory data. But there are many more possibilities that can all generate surprising new insights. The more data that you collect, the better the analyses will be and the more valuable the information derived from it will be. Data can come from any channel or product, also from those not previously thought about. Therefore, think out-of-the-box when you determine which data you want to capture.
Who should be the sponsor of the big data project?
There are many reasons why a big data project could fail and be taken of the agenda again. Especially in the beginning the returns on investments made can be unclear and could potentially be negative. Management buy-in ensures that the project is not stopped before any real results can be shown. Therefore it is important to find the right sponsor for your big data strategy.
This should be someone within the organization that understands all different departments, is able to have a helicopter view of the project and is high enough within the company to direct and align different departments. Preferably not someone from the IT department. After all, IT is merely a means to an end to achieve a big data strategy defined by the organization. As IT is merely supportive, senior management or the board should be involved and support the decision to move forward with big data.
Of course there are many more important questions that all deserve a lot of attention when developing a big data strategy, but hopefully these questions get you started when implementing a big data strategy.