Most enterprises don’t need a big data strategy, rather they need to develop a robust business strategy that incorporates big data. It’s important because valuable data insights can help differentiate customer experiences, uncover new business opportunities, and optimize key business processes.
Once you incorporate big data into your business, pretty soon you will end up with a huge data lake. But to keep leveraging data and analytics to make key business decisions, after a while it will become critical to determine a data lake business maturity model.
To achieve this, you have to first start thinking about data lakes as more than just a technology for the data repository. This type of mindset can work against your business goal and hinder your organization’s ability to leverage the data (to derive maximum business value).
When that happens you also lose opportunities to monetize the data and mitigate security and compliance risks. So how should you go about it? They key here is to develop a data lake maturity model.
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The data reservoir stage
Data lakes have three stages of maturity with the first one being the data reservoir (where apps and data stream into the data lake from multiple sources). When this happens, you will have data structures with in-depth detail and summary levels.
During this phase, datasets will be easily accessible in a centralized platform. If you have been in the data reservoir stage for a while, it’s time to let go of the old model of offloading as much data as possible.
While this approach was originally adopted to cut costs related to performing analytics, it ended up hindering the ability of businesses to build highly scalable elastic platforms.
The data exploration stage
During the data exploration stage, the process of expanding the data lake to enhance and enrich datasets takes place in a bid to cleanse the data. This is where Hadoop enrichment tools come in, but utilizing them without a plan can lead to challenges down the road.
There are many variables that contribute to the creation of “anti-patterns” like having too many Hadoop distribution clusters all over the organization. As each department deploys their own Hadoop, the data will expand but it will end up creating multiples siloes of data.
As a result, this approach defeats the purpose of big data analytics. It’s the same when it comes to implementing too many restrictions on what can be viewed, accessed, and analyzed.
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A robust data lake model needs to be highly secure but should enable seamless access to multiple stakeholders. What’s more, it also has to be highly elastic in nature to support scalable technologies and data management services to derive real value for the business.
Analytical lake stage
In the third and final stage of the data maturity model, you will need to be enabled to combine exploratory analytics with the existing data warehouse, data structures, and data migration (both in and out).
This approach will create a schema that can be read while processing to allow you to bring multiple data structures to the singular analysis platform to rapidly generate a single dataset. In this scenario, both exploratory analytics tools and historical tools can be combined to get the best results.
However, accessing business value will heavily depend on building the right architecture. The best approach here is for IT to partner with business to ascertain what solutions are required by to achieve business goals. This can even come in the form of templates for common tools like Spark.
It should also empower users to self-provision data to further accelerate the time to insights. Most enterprises have achieved this via virtualized or containerized deployments of big data environments. This translates into virtualizing the tools and automating the deployment of deployments so these environments can easily scale up or down depending on business needs.
In the end, it needs to be built to embrace the unique characteristics of digital assets like data and analytics. It’s not something that won’t deplete or wear out, so it can be (re)used across the organization for an infinite number of use cases, cost-effectively.
The value of data is limited until it’s analyzed. What’s more, the value of data is enhanced when it’s analyzed in real-time to enable immediate action.
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