{"id":3272,"date":"2022-05-05T08:24:37","date_gmt":"2022-05-05T06:24:37","guid":{"rendered":"https:\/\/www.pue.es\/blog\/six-keys-to-implement-a-successful-data-strategy\/"},"modified":"2024-01-04T15:51:31","modified_gmt":"2024-01-04T14:51:31","slug":"six-keys-to-implement-a-successful-data-strategy","status":"publish","type":"post","link":"https:\/\/www.pue.es\/blog\/en\/six-keys-to-implement-a-successful-data-strategy\/","title":{"rendered":"Six keys to implement a successful data strategy"},"content":{"rendered":"

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.<\/span><\/h1>\n

In this article we are going to talk about the planning and design of a data strategy.<\/span><\/p>\n

When it comes to establishing a good <\/span>data strategy<\/b>, 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.<\/span><\/p>\n

But Data Driven decision making not only requires tools and technological support for its implementation, but also a transformation of the business culture. <\/span>Putting data at the center of companies involves much more than setting up an architecture<\/b>. It is a transformation project itself.<\/span><\/p>\n

The first step to initiate a transition towards a Data Driven company is to <\/span>understand the value of data and to define what we want to know about it<\/b>.\u00a0<\/span><\/p>\n

There is no universal recipe, but a number of aspects that must be taken into account when setting up a project involving data:<\/span><\/p>\n

    \n
  1. Set short-term, realistic and achievable goals.<\/span><\/li>\n
  2. Establish data standards that are as open as possible, allowing interoperability and scalability.<\/span><\/li>\n
  3. Choose redundant and durable storage.\u00a0<\/span><\/li>\n
  4. 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.\u00a0<\/span><\/li>\n
  5. Do not lose the source of the data. Having all the metadata stored will allow us to go back whenever necessary.\u00a0<\/span><\/li>\n
  6. Ensuring data integrity and protection is fundamental. This is something that should not be compromised under any circumstances.<\/span><\/li>\n<\/ol>\n

    But all these depend on each use case and how to implement it in an organization.<\/span><\/p>\n

    If you are interested in <\/span>going deeper into the keys to define a good data strategy<\/b>, 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.<\/span><\/p>\n

    With the participation of Jaime Bala\u00f1\u00e1, 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.\u00a0<\/span><\/p>\n

    Don’t miss this one! \ud83d\udc47<\/span><\/p>\n