what is a technology agnostic data lake solution how does it benefit you scaled

What is a Technology-Agnostic Data Lake Solution? How Does it Benefit You?

A data lake is a consolidated and central data repository of all structured, unstructured, and diversely-sourced data relevant to an organization. It harbors, nourishes, processes, and leverages any data that has value from the standpoint of compliance, analytics, CRM, ERP, business intelligence, and innovation. There’s nothing like the calming sight of a data lake around an enterprise. Except, of course, for that scenario where all this fluid and freshwater data is pointless. Because you cannot drink it.


The need for agnostic data lakes

And why can you not drink all this enterprise data? You want to pour it in because it cannot talk to the vessel. This enterprise data only fits in a specific glass. Put it anywhere else and it will either spill or spoil.


But that’s not how a business works, right? A business works across many stakeholders, supply chains, ecosystems, and geographies. In today’s ultra-digitized, super-connected, and globalized world- can you imagine being trapped in these boxes because of a data fluency issue?


The Philips Future Health Index 2021 clearly shows the implications of poor interoperability in an industry like healthcare. It turns out that difficulties with data management (44 percent) and lack of interoperability and data standards (37 percent) are the most significant barriers to adopting digital health technology in hospitals and healthcare facilities. This reality echoes in a recent annual Physician Sentiment Survey by Harris Poll. athenahealth, Inc. Merely 24 percent find it easy to send and receive patient data with another provider on a different EHR. Also, 80 percent feel increased stress due to the inability to share data between information systems. The main hurdles to improving physician connectivity are coordination among different facilities/health systems (65 percent) and lack of data standardization (57 percent).


No matter how huge or majestic or clever your data lake is – it will be a waste of time, money, and effort if it is not technology-agnostic. You will not be able to drill into all those data insights lying inside these data lakes.


So how can you teach these lakes to flow seamlessly into other ponds and waterfalls? Simple. Use a technology-agnostic approach for data lakes from the very beginning. Here’s how.


Understanding technology – agnostic data lake solutions

A data lake solution is technology-agnostic if it can pull in data without hiccups from any format, application, or data environment. It is technology-agnostic if it can process and store data irrespective of the formats or modules or architecture they were, earlier, created or stored in. Technology barriers and proprietary walls do not inhibit these data lakes. They are conversant enough to fit in and work with any environment, structure, version, and model.


In today’s era, many organizations fail to unfurl the maximum power from their technology investments because they get trapped in vendor lock-in issues. They get constrained due to dependence on specific technologies. Despite the promise of limitless efficiency and performance, the feet of most systems get iron-chained due to their inability to play seamlessly with other pieces outside their boxes. Hence, it helps an enterprise gain the maximum benefit from data lakes by using technology-agnostic data lakes. And what are they like?


How to spot/create a technology-agnostic data lake?

One needs to know some key characteristics and principles of technology-agnostic data lakes. If you can check this list well, you can enjoy many benefits. So assess your data lake well for:

  1. Flexibility and adaptability
    1. Ability to incorporate various data storage and processing technologies
    2. Easy integration with existing data infrastructure and tools
    3. Clarity and grip on various industry standards
  2. Vendor neutrality and cost efficiency
    1. Freedom to choose best-of-breed technologies and solutions
    2. Avoidance of costly migrations or re-architecting in the future
    3. Cross-platform ease of working
  3. Future-proofing and scalability
    1. Accommodation of evolving data management and analytics needs
    2. Ability to scale storage and processing capabilities as data volumes grow
    3. Interoperable pathways for swift movement across data buckets
    4. Protocols that make it simple to jump from one system to another

Also, note some important considerations for adopting a technology-agnostic data lake solution.

  1. Evaluate current and future data management requirements

    – note every possibility and scenario in advance.

  2. Assess existing technology landscape and integration capabilities

    – check for all possible alternatives- do not be myopic on the big names and solutions.

  3. Identify data governance and security requirements

    – strong safety measures and management safeguards should always be on the front burner.

  4. Collaborate with stakeholders and IT teams for successful implementation

    – ultimately, people make magic out of any solution. Invest in them.


  • Leverage cross-industry and healthcare open standards to enable innovation and collaboration and prevent lock-in.
  • Prioritize open-source software built by a community of developers and organizations over proprietary options.
  • Follow a data fabric model for integrating information across systems and breaking down silos.
  • Enable an open ecosystem of partners and vendors to foster innovation and allow collaboration and competition.
  • Integrate operational controls and audibility for consistent management.

To implement a technology-agnostic data lake solution, an organization needs to keep in mind some crucial facets:

  1. Design a flexible and scalable data lake architecture

    – make it ready enough to work with any size or format.

  2. Select appropriate data storage and processing technologies –

    the underlying storage architecture and frameworks matter a lot in creating a truly agile data lake.

  3. Implement data governance and security measures –

    do not forget the emphasis on safety in the ambition to master flexibility. The implications of the trade-off between flexibility and security should be clear – with security always as a non-negotiable lever.

  4. Ensure compatibility and integration with existing systems and workflows –

    While outward and downstream interoperability is essential for a data lake, it’s also vital to build in cross-over ease for existing systems and upstream processes.

Also, make sure you are aware of some critical challenges. Pay attention to them in advance and adopt timely mitigation strategies. Be proactive in addressing potential challenges in implementing a technology-agnostic approach. Do implement mechanisms to overcome interoperability and integration issues – at the onset. These decisions should not be taken after taking the deployment leap. Make sure these parachutes open; otherwise, you will be caught mid-way in the air with no clue what to do next. Have your vendors on the same page. Do not let confusion creep in. Communication and clarity are keystones for garnering actual interoperability. Document and discuss everything beforehand to avoid disputes or dead-ends. This is where the habits of proper planning and stakeholder buy-in come into play very strongly.


As we can see, a technology-agnostic data lake solution can help you go far and deep with your data investment and solidify your enterprise data’s power. According to Mckinsey’s Technology Trends Outlook 2022, industrialized machine learning (ML) uses software and hardware solutions to accelerate ML development and deployment and support performance monitoring, stability, and ongoing improvement. This space was as huge as $5 Billion in Investment in 2021. But making significant investments will not help companies pull out any rabbits from the data lake hat. More so, as ML’s performance hinges a lot on the volume of apt data available for learning. This data can get severely restricted in the absence of interoperability.


It’s an era where you ‘have’ to embrace flexibility and future-proof your data lake strategies. It’s not a choice. It’s a necessity. To master this challenging path of data-driven decision-making and innovation, it’s better to take help from real experts.


Some experts give you end-to-end support in getting, processing, harnessing, and innovating on data. They help you to visualize, and map out, data into action steps. They make your complex data more viable and accessible in a single-point data view. They can convert all that raw and sprawled-out data into one digestible format, painting a clearer picture of what’s happening. And with breakthroughs in machine learning and deep learning, they can uniquely address a given vertical, industry challenge, or opportunity like Carvewing – which is packed with machine learning and AI solutions, data-driven dashboards, visualization tools, and solutions.


Data lakes can be wonderful sinks of absorbing inefficiencies and churning out productivity streams and high business performance. Just make sure they are not trapped behind iron dams. Rest will be easy.

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