Defining a data strategy that puts data at the center of the company is nowadays essential. Being able to extract value from data allows us to make strategic business decisions, improve process efficiency, gain a competitive advantage and identify opportunities for improvement, among many other benefits.
In this article we are going to talk about the planning and design of a data strategy.
When it comes to establishing a good data strategy, it is not about surfing the technological wave, but about being clear about the value of the data and the objective we are looking for as an organization. There is no good or bad architecture or platform; there is the right one for each kind of project, need and objective.
But Data Driven decision making not only requires tools and technological support for its implementation, but also a transformation of the business culture. Putting data at the center of companies involves much more than setting up an architecture. It is a transformation project itself.
The first step to initiate a transition towards a Data Driven company is to understand the value of data and to define what we want to know about it.
There is no universal recipe, but a number of aspects that must be taken into account when setting up a project involving data:
- Set short-term, realistic and achievable goals.
- Establish data standards that are as open as possible, allowing interoperability and scalability.
- Choose redundant and durable storage.
- Data must have a governance, both at audit and authorization level. It is just as important to define who can access the data as it is to ensure that this process is fully audited.
- Do not lose the source of the data. Having all the metadata stored will allow us to go back whenever necessary.
- Ensuring data integrity and protection is fundamental. This is something that should not be compromised under any circumstances.
But all these depend on each use case and how to implement it in an organization.
If you are interested in going deeper into the keys to define a good data strategy, don’t miss this debate organized by ComputerWorld Spain, moderated by Marlon Molina, computer engineer and disseminator specialized in Big Data, Cybersecurity and Artificial Intelligence.
With the participation of Jaime Balañá, Solutions Engineering Manager at NetApp Spain; Miguel Angel Perdiguero, Cognitive & Analytics Associate Partner at IBM, and our teammate Ramon de la Rosa Falguera, Big Data & Cloud Architect at PUE.
Don’t miss this one! 👇
Big Data and Cloud Computing Technology: the perfect match
Big Data and Cloud Computing are two technologies that fit perfectly together. In Big Data we have a volume of data and processing that is often difficult to predict; and Cloud Computing allows us to scale, destroy and reassemble an infrastructure when necessary, thanks to its flexibility and agility.
The customer is the one who decides whether to have an infrastructure in the cloud or on-premise. But the important thing is to do it with the right tools to make it as simple as possible. And we can only achieve this by understanding the customer and really knowing what his challenges are.
Do we have to wait until we have a high level of data or maturity to be able to use a data strategy?
Setting short-term, realistic and achievable challenges helps organizations to embrace a data strategy. Starting with a small pilot and testing its effectiveness is recommended, and the strategy to follow.
We should not go beyond what we are capable of as an organization. And not because the technology is not working for us, but because organizations are sometimes not prepared to take it on. We must verify the mission and vision of the data, look for best practices without being individualistic and embed data projects across the business.
“What we recommend is to define a first very clear use case that can be a success both internally and externally, and that will allow us to define exactly the framework that is going to be used within the organization”.
Ramon de la Rosa I Big Data & Cloud Architect at PUE.
How can we know if a company is being successful in its Big Data projects?
One of the aspects to look at is the ROI, the profitability of the project, to see if the objective has been achieved and if it is worth continuing with it.
Another would be to see if the desired objective has been achieved: has the data been used as it was initially planned?
Finally, the maturity of the project is another key indicator of success. A small project that has grown over time is undoubtedly a clear evidence of effectiveness and success.
The road to the Data Driven enterprise
As we mentioned earlier, the value of a Big Data project has to come from the line of business, from where the data is generated. Then, together with the IT department, work will be done to make it successful.
In some cases, the intention is to apply Big Data to data that is not even being stored. And that is a mistake. In that case we must start, for example, by digitizing the processes that allow us to get that data. And from there, define different strategies.
Big Data should not be a hype. Big Data is a technology. And, therefore, it is key to have the right partner that can help us define a quick battle to win, and thus be able to establish a roadmap to achieve greater challenges.
There are no single, standard solutions when it comes to data. At PUE we help you define, plan, develop and implement solutions based on your project and organization’s objectives and needs.
