What is Big Data Assurance?
Big Data Assurance is about providing a strategy, deriving a process, and aligning the right tools and resources required to address the problem areas outlined above. However, in order to create a Big Data Assurance strategy, it is not only important to understand the pain points observed in current implementations, it is also critical to understand the nature of Big Data implementations seen across enterprises. We have observed that there are currently two primary flavors of enterprise implementations that handle Big Data:
- High volume, velocity, and variety of data repositories
- High performance data processing engines
Each flavor of implementation requires a different Assurance strategy to address the issues that will be faced. For Big Data platforms used as data repositories (such as Data Lakes), the primary area of concern relates to the correctness, completeness, and timeliness of the data stored from various sources. To ensure this, two primary tasks need to be performed – first, ensure that each data source provides data that is correct and complete (compared to the source),What is Big Data Assurance? and second, ensure that the quality of data on the system meets the standards that meets governance and timeliness policies required.
Assuring quality on high performance Hadoop platforms require a slightly different approach in addition to the tasks associated with the first type of implementation. In order to assure quality on this type of implementation, it is not only critical to ensure the quality and correctness of data that is stored on the data system, it is imperative to test (functional and non-functional such as performance) the various algorithms that are written to cleanse, process, and transform the data that will ultimately provide the metrics, dashboards, reports, and, other consumables required.
Moreover, given that Assurance tasks in Big Data implementations involve working with large and varied amounts of data, it is imperative to have an automation strategy to ensure that resources don’t spend too much time and effort performing mundane, yet, critical tasks. Despite this, most implementations today primarily perform all testing tasks in the Big Data world manually.
Current State of Assurance in Big Data Implementations
The level of maturity in current Big Data implementations show that there is a huge scope for Data Wellness/Data Assurance in current Big Data implementations. This is further proven by the fact that over 52% of respondents cited that the market currently lacks good Assurance tools and services. Only the lack of data analysis tools and services (62%) was featured as a bigger service/tool-related gap for enterprises. The demand for professionals with Big Data Assurance experience coupled with the lack of tools and services has resulted in over 77% of the respondents to the survey struggling to find individuals who have the required skills to perform Assurance using Big Data technologies. Across verticals, Utilities (40%) have taken the lead in hiring third parties for Big Data Assurance, whereas Hi-Tech (67%), Banking and Insurance (50%) companies display a high preference in keeping Assurance activities in-house.
Results also indicate that enterprises are on the lookout for testers who have expertise in data analysis (88%), Hadoop development (58%), and tool-specific work experience (52%). The profile of individuals who will perform the role of testers is skewed towards those who can develop as well as test. Although only 29% of our respondents stated that all Big Data Assurance will be tightly coupled with development, 49% believed that there will be a role for testers who have development and scripting skills as well. Only 22% of our respondents believe that there will be standalone Assurance opportunities on Big Data projects.
Enterprises today appear to be in splits about how to fulfill the assurance resource requirements on projects. Only 29% of our respondents are either currently working with third-party service providers or considering an engagement with third-party providers whereas, 32% of our respondents expect to hire professionals outright from the market. Almost 39% of our respondents are unsure about what strategy they need to adopt at the moment.
Although organizations are not planning to look for people resources for their Big Data projects, it appears that enterprises will look at engaging third-party tools and consulting services. This can be inferred by the fact that 80% of our respondents will engage with third parties for their Big Data Assurance requirements over the next three to five years. Overall, companies with budgets between $50M and $100M are most likely to outsource Big Data Assurance to third-party vendors. This shows that there is a lot of scope for Big Data Assurance tools and services (especially Big Data Assurance as a service) in the near term for third-party vendors.
Benefits from Implementing a Holistic Big Data Assurance Strategy
From the results mentioned above, it is clear that organizations are unable to implement a holistic Big Data strategy. This situation could have arisen due to a lack of understanding on how to integrate an Assurance strategy into current implementations, along with a paucity of skilled resources, services, and tools available to address the specific challenges posed on current Big Data implementations.
The best method to integrate a Big Data Assurance strategy – into existing implementations – would be to understand the essentials of what it involves, and how it will help organizations overcome current challenges. A Big Data Assurance strategy helps enterprises derive maximum value from their Big Data implementations. A holistic strategy should primarily deal with:
- Testing the quality of the data right from the source through the use of a data quality index for each data source/stream
- Testing the quality of the platform and the algorithms/ technologies used to transform the data from its raw state to a consumable state
Despite the abundance of technologies and processes in the market today, there is a need for consolidation and industrialization throughout the entire Big Data lifecycle. Currently, there are no solutions available that provide a holistic approach to handle the various challenges faced by enterprises implementing Big Data. Moreover, there is clear need to identify a technology stack that will allow enterprises to aggregate, ingest, analyze and process, and consume data effectively.
But not all is hazy. There are solutions available that help organizations create value in their Big Data implementations. These solutions establish a measurable and repeatable methodology for various technical and process-related challenges and help identify the key activities that require maximum attention. Validation and verification of these activities will ensure that enterprises can extract value from their Big Data implementations that are also imperative for any solution. Enterprises need to identify the right solution that fits their Big Data implementation strategy. This will ensure that they find light in the chaos.