In this blog, Exasol’s CTO Mathias Golombek discusses how to implement a data democratization project in your organization. It covers:
– Why you need a data evangelist
– The importance of a data strategy
– The role of education and upskilling
– How to approach your tech stack
In the first part of this blog series, I likened the French revolutionary ideals of freedom, equality and brotherhood – or liberté, egalité, fraternité – to the mindset businesses should adopt when thinking about how they manage, distribute and consume data within their teams.
Here, I’ll be taking that a stage further and looking at the practical steps any data democratization revolutionary should take to implement a robust program.
For me there are five clear stages to creating a data democratization program. They are:
1. Assign a leader – find your data evangelist
I’m an advocate of recruiting a Chief Data Officer (CDO) to take ownership of a firm’s data. KPMG suggests that organizations with a CDO are twice as likely to have a clear digital strategy. The remit of the CDO is to drive the business forward across the board, from revenue growth and advancing internal innovation to improving operational efficiency. Achieving data democratization relies on having the right skills in your people and triggering their imagination and innovative ideas. The CDO is one of the best-placed individuals to make knowledge of data an integral, normal part of the everyday life of an organization.
2. Avoid political turmoil – develop an overarching data strategy
Organizations need a coherent data and analytics strategy in order to extract the insights they need to move the business forward. The most effective data strategies are integrated with the overall business strategy from the start, and establish common and repeatable methods, practices and processes to control and distribute data business-wide. If the whole organization is involved from the beginning, colleagues will be more inclined to help drive a strategy forward.
A robust data strategy and culture that harnesses data democratization also requires the right infrastructure to support it. When choosing a deployment model, organizations need to consider factors such as speed, cost, future requirements and the types of workload expected. Therefore, it’s important for businesses to make this decision after the data strategy is in place, to fully evaluate whether on-premises, cloud or a hybrid approach is the right option for what they want to achieve.
A hybrid cloud approach can often be the most efficient. It allows organizations to manage sensitive workloads on-premises, but also use the cloud – which is powerful when it comes to delivering large volumes of data to lots of people in real-time.
But remember, no matter how brilliant your strategy and infrastructure, it is worthless if employees don’t buy into it.
3. Educate the people – develop an employee education process
As you provide more teams and departments with access to data, you’ll need to build training into the process. You’ll need to champion data literacy and try to teach data as a second language within the business. Some firms go further and develop a Data Center of Excellence (CoE).
Regardless of their level of technical expertise, everyone working with data can gain confidence by familiarizing themselves with the components of the analytics stack in your organization and the best practices that come with it.
AirBnB is a great example of a company doing this well. Despite having a Loading...data science team of more than 100 people, the company’s fundamental belief is that every employee should be empowered to make decisions informed by data. In an effort to scale its skillset, AirBnB developed its own inhouse Data University and curriculum for its team.
At Exasol we offer our customers free training courses on the Exacademy to help them empower people across the business with additional knowledge, skills and understanding.
4. Build on strong foundations – implement the right tech stack for you
Getting your tech stack right – or as right as possible – is a crucial element of a data democratization effort. When scoping your stack, keep in mind where you want to get to in the future and how you can enable more people to work with data to drive insights and support decision-making. During this process you can test the use cases of different departments. Rather than giving demonstrations, let people experiment with their own data and tools to give them a clear idea of what’s in it for them.
The co-existence of multiple analytics tools within different teams and departments can add a layer of technical challenge, but it’s one worth solving. Having teams with various tools at their disposal will provide greater opportunities for the right tools to be used at the right time for the right purpose.
Look for a database like Exasol that offers you a fully flexible integration framework, so you can use any front-end analysis tool alongside any data science language. Having all the data for analysis in one place and being able to connect with various tools means everyone can benefit from ultimate performance and Loading...advanced analytics capabilities. You can read our guide to this here.
5. Write your own history
When considering data democratization, I urge you to reflect on your own frustrations and experiences with data access. Whatever steps you take towards it, remember that every organization is different. From the data strategy, to the tech infrastructure to the types of data skills employees will need, no two set-ups will be the same. You need to take the approach that’s right for you.
Starting your journey towards redefining how you and your company work with data relies on everyone being connected to data, with no performance issues. That’s why at Exasol we strive to be as agile, extensible and platform-agnostic as possible. Whatever analytical tools or data science languages you use, whatever your deployment method, and no matter where you collect and store data the focus will always be on delivering the performance, ease of use and flexibility that gives you the biggest return on investment.
I’ll leave you with those thoughts for now, but in my next blog I intend to explore the link between data democratization and using data meshes as a means to connecting siloed data and creating a self-service data infrastructure that makes data highly available and easily discoverable for the people who need it. Watch this space.
Mathias Golombek is Exasol’s CTO. He joined in 2004 as a software developer, led the database optimization team and became a member of the executive board in 2013. Although Mathias is primarily responsible for Exasol’s technology, his most important role is to create a great environment in which smart people enjoy building powerful products.