Big Data Analytics & Data Lakes

Breaking Data Lake Myths

Pharma companies realize the value of data-driven insights and decision making and employing best-in-class technology and infrastructure to enable them. So, in recent years, pharma companies are investing heavily in big data solutions. However, these systems do not yield the kind of value expected from the investment. Some of the reasons include:

1. Lack of technical expertise in choosing the right technology
2. Struggle in managing perceptions of the outcome expected from such investments

In such cases, both the business and IT teams end up spending too much time researching the options to come to a common ground.

Data Lakes have been around for some time now intending to capture the data first and find a use for it later. It brings to pharma companies the potential to store and leverage critical datasets that support multiple use cases, including optimization of the patient journey and real-world outcomes, gross-to-net sales optimization, digitization of smart contracts (specialty market), IoT data, EMR/EHR data amongst others. While many are reaping the benefits of an agile, flexible and cost-effective Data Lake solution, some organizations are still wary of dipping their feet into the lake and making the most out of what this concept has to offer.

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Busting some myths that surround the vast scope of Data Lakes:

Myth #1: Data Lakes are black box products.

Fact #1: Data Lakes are built on scalable architecture delivered on a combination of technologies.

Pharma companies are constantly experimenting with different data vendors for new-age data sets to derive valuable insights as the industry shifts focus to value-based care and patient-centric outcomes. Contrary to the popular understanding of Data Lake being too tech complex and too farfetched for leaders to get control off, a Data Lake architecture brings in the necessary flexibility and efficiency needed to collect, process, and analyze these large and complex data sets. It is an approach that organization leaders can use to put data at the heart of the organization’s operations and includes governance, quality, and management of data, thereby enabling self-service analytics to empower data consumers.

Data Lakes are not black box solutions; they are a scalable architecture delivered through a combination of technologies. With deep domain knowledge of the pharma industry and use cases, it is possible to build an evolving Data Lake to suit the business needs. Technologies that integrate the growing business, people, scale, and data needs can be added to a Data Lake to match-up to the needs of the organization to provide control and depth that they seek.

Myth #2: Data Lake is a dumping ground.

Fact #2: Data Lake is highly governed to ensure maximum value is generated out of the data.

The movement to cloud has made it possible to store massive volume of data at a low cost, i.e., every piece and phase of information, right from drug development to commercialization, can be gathered in one place rather than letting them reside in silos, separated by the department. By creating ‘sandboxes’ of data in their work, data analysts and data scientists can bring data freely in and out of the lake, so companies need not hold back to push data to the cloud. That said, a Data Lake is not simply a dumping ground. It is a reservoir for all types of “raw” data, including structured, semi-structured and unstructured; the data structure and requirements aren’t defined until the data is needed. Cataloging of data, quality, data governance, and management processes are important aspects of a Data Lake. So, a Data Lake is a solution that can store both structured and unstructured data, containing both raw and processed data zones, in a highly governed set up to generate the maximum value of the data that resides on it. Governance on data within the lake is important to preserve the integrity of data to avoid the lake from becoming a mere data dump.

Myth #3: Data Lake implementations take a long time and are complex

Fact #3: Data Lake can be implemented in phases and its complexities depend on a company’s data landscape.

Data Lakes can be implemented in phases, and with the right infrastructure and governance setup, data scientists can be empowered to start using the lake within a short time. Phase-wise implementation:

  1. brings agility into the data landscape without making the business teams wait for insights, and
  2. enables timely course correction to deal with the changing requirements

A Data Lake implementation is not complex by itself; its complexities depend on a company’s data landscape – the number of therapeutic areas covered, the number of brands, and the variety of data captured. With teams that have the necessary experience in the functional and analytics area within the pharma domain, implementation time for Data Lakes can come down drastically. Further, the functional and analytics experience enables the Data Lake users and data scientists to have secure, self-serve access to terabytes of expanded data universe of pharmaceutical, clinical, and real-world data, empowering them to complete several times more analysis than traditional data warehouses. The Data Lake of clean and pre-programmed data can be further integrated with data sciences platforms to implement high-end data analysis and machine-learning use cases.

Myth #4: Data warehouse is no longer needed if a Data Lake is implemented.

Fact #4: Data warehouse and Data Lake serve different purposes.

The only real similarity between a data warehouse and Data Lake is the high-level purpose of storing data. Data Warehouse (DW) is a repository of structured data from disparate sources that have been processed for a purpose, while a Data Lake is a pool of both structured and raw data, the objective for which is yet to be defined.

An enterprise data warehouse enables companies to implement enterprise-level reporting and provide a single-version-of-truth for all business units.  On the other side, built on a big data framework, a Data Lake can incorporate multiple DWs, plus additional data sources such as those from social media, raw data uploaded on Excel or IoT. With data governance embedded, it simplifies trusted discovery of data for users throughout the organization. It empowers data scientists to experiment and explore new data sources with unprecedented scale and flexibility.

Conclusion

A Data Lake provides data scientists with a huge playground to play around with data and garner maximum value out of it by gaining free-flowing access to secure, trustworthy, structured and unstructured data. When supported by deep pharma domain and dataset expertise, you can get the most of a Data Lake and build a scalable and customizable solution as per the organizational need, significantly bringing down the time and effort. By enabling pre-processing in the cloud using suitable tools, Axtria’s Big Data Framework transforms your business and builds a culture of exploration and experimentation. 

“By improving analytics efficiency by up to 90%, Axtria solutions allow data scientists to focus on analysis and generating insights that drive business growth. Learn How”.

 

Tags: Big Data, Information Management, Cloud Data Management, Big Data Framework, Data Lakes, Big Data Analytics

Megha Kapadia

With over 13 years of experience, Megha is currently working on solutions based on Data Warehousing, Data Lake, Master Data Management (MDM), Business Intelligence (BI) and Commercial Analytics. She is a techno-functional consultant with extensive experience in P&LS, Retail and Energy domains. She holds a Bachelor’s degree in Electronics from Mumbai University.

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