Want to innovate like Netflix?

While it’s more straightforward for companies that started off digitally native to think data first, it is a lot harder for legacy companies that are just starting out on their digital transformation journeys.

One company that is truly excelling in its data strategy is none other than media-services provider and production company, Netflix. Unlike its media rivals, who are legacy behemoths, Netflix uses data to drive every decision, from content development to its creative and marketing strategies. The data Netflix collects helps the company glean deep insights into every customers’ preferences and usage habits in real-time, empowering them to make almost all strategic and tactical business and operational decisions based on data.

There’s no doubt Netflix is a modern data company in today’s digital era, and its impact is felt across multiple industries. How can companies take a leaf out of Netflix’s data philosophy and begin their journey towards being truly digital?

Step 1: Ensure your data is accessible, easy to discover, and easy to process for everyone

In most organisations, the access and availability of data is controlled and managed through a centralised group of database administrators (DBAs) with no self-service capabilities for practitioners who need data. Data access and availability remains a set of ticket-driven manual processes with excessive workloads being put on the DBAs to do repetitive low-value tasks. But data should be accessible by those who need it, when they need it, and where they need it, in real-time. Companies should be embracing technologies and policies that empower their data practitioners to get the data they need, when they need it.

In addition, most companies lack even basic inventories of their data. The absence of good data classification makes it difficult, or in most cases, impossible to manage and govern data based on its risk-value profile and category. All the data ends up being treated in the same manner, with the same governance overhead and restrictions, which makes data less usable and easy to process than it can be, and of course, makes it difficult to find the right data. You simply can’t manage what you don’t understand.

Step 2: Remember that the longer you take to find the data, the less valuable it becomes

As organisations evolve to newer, modern technology stacks without retiring older stacks completely (still waiting on the mainframe to go away as predicted in the 1990s), or through acquisitions where they bring in an entirely new set of platforms, data is sprawled across heterogeneous sets of data stores – many of which are not compatible with each other and may even have multiple versions of the same database in use in different parts of the organisation. By spreading data in this way, creates challenges around data protection and governance, which is a pain point for the business. Multiple data systems storing the same information can lead to expensive data and infrastructure sprawl.

This sprawl of data store technologies means that it take businesses much longer to find the data and the sprawl further expands with the addition of cloud-native, fit-for-purpose data stores that developers are beginning to adopt as they develop new cloud-native applications, which have a different need as opposed to traditional databases. This is because spreading the data creates a lack of visibility and expertise, slow manual processes and issues surrounding compliance and regulation.

Step 3: Visualise your data - whether your dataset is large or small, being able to visualise it makes it easier to explain

With the rise of DevOps, monitoring and logging of all software components in your tech stack has become critical. Typically, monitoring tools are used to facilitate feedback between the operations and development teams during software development. If there are consistent errors for certain components, the teams are able to communicate and collaborate to resolve those issues. In response, DataOps is emerging as an approach to address these data-related challenges. It spans change across process, technology, and people to help an organisation become a data company, like Netflix and address data-related challenges.

It's important that organisations have a proactive incident response policy like this in place and use it when things go wrong — because they will go wrong. Data never lies, so organisations shouldn't base their decisions on hunches.

Data and visualisation tools, like Splunk, take threats we cannot perceive directly and make them accessible and easier to explain to our human sensory system through correlation, time mapping, and a graphical display that echoes our own visual systems. This means data must become a real-time resource, available at the fingertips of every user in a self-service format they can consume as insights. Driven by this need, we see an overwhelming demand to shift toward a way of working that enables data agility and accessibility. 

Data is the winning ingredient

If there’s one thing companies can take away from Netflix’s data philosophy, it is the need to shift towards a data-driven culture.

To drive innovation and stay ahead of the competition, today’s enterprises need to master data. Organisations must take a holistic approach to nurturing a data-driven culture by rethinking their data management and governance processes as well as their data-related technology stacks to succeed in today’s digital-first business landscape.

A foundational change in how data is accessed, managed, secured, and leveraged across the enterprise is essential in dramatically modernising your data strategy while achieving regulatory compliance with today’s new privacy laws and growing number of data breaches.

Data is the key to today’s businesses and modern-day software development. Leveraging data to drive innovation, identify new business models and add new value-added services and offerings delivers meaningful value to customers.