Big data as defined in the Wikipedia, Big data is a term for large or complex data sets that are a constantly moving targets for the traditional strategies, tools for processing and managing them via traditional data processing applications which are inadequate to deal with them. Challenges include analysis, capture, data curation, search, sharing, storage, transfer, visualization, querying, updating and information privacy.
Academically this sounds good as most of the organizations are banking on the data to provide them the insights on their use cases, more predominantly via strategic usage of the infrastructure, tool sets and strategic uses cases that drive presentation layer of the processed data, i.e. information. The process though defined and known is not as simple as stated. Before we dwell upon the challenges let us take a quick read on the four Vs of the big data. i.e.
As you must have gathered that the bigdata is well beyond the defined data sets that traditional systems have dealt with and thus demand a separate handling, farming and processing strategy, if you miss a single thought process that encompasses all the tenets of bigdata the results could be irrelevant and unfit for the business use. This brings us to think about an additional called Value of this initiative
Why search for fifth Value?
Simply put, Data-driven decisions are The decisions. These insightful decisions are the business enablers and rests on the basis of evidence rather than intuition or gut feel there are more tenets as to how data will be defined ( data classification), captured ( Data Ingestion) then normalized ( Data Integration) and then transformed ( Data Visualization & Analytics) to arrive at the Value of the eco system we just described. Few aspects that needs consideration such as..
looking at the vastness of these aspects it is more practical to relook at your bigdata strategy and focus on building the Enterprise data hub at the first logical step. There are quite a few advantages to build Single source of truth than relying on the isolated solutions that actually create more data silos in an organization while attempting to deliver Analytics or Dashboards from their native ( read proprietary) platforms locking the data to be used only with or via their platforms. Beyond these aspects there are challenges that too must be dealt with while building the Enterprise Data Hub such as.
More on Why Enterprise data hub?
We touched upon earlier on the need of having an “Enterprise Data Hub let us gain more insights on the concept and benefits of having one. Since it is complementing the modern data strategy, and enterprise data hub enables collection and storage of unlimited data cost-effectively and reliably respecting diverse set of users, use cases and integrated analytic solutions. The biggest advantages are
Longevity & Sustenance Since the enterprise data hub has been built on the zero data loss and optimized for performance via distributed computing the traditional backups and offsite storage is no longer relevant, the systems such as Hadoop has built in resiliency and fail proof tolerance for processing sets it apart by design rather than external operational ability.
Faster and Agile The data hub has ability to ingest, transform and process the data on the same dataset simultaneously it is limitless and closer to real time storage and analysis making it more relevant for the current scenarios of use cases, truly online.
Availability & Compliance - The agility in the real time processing and storage and zero loss principle complementing archival compliance for internal and external regulatory demands. Since entire data is available for processing without additional overheads the system remains truly reactive to the demands of the business for comprehensive full data set computing, and guaranteed low latency at any scale which seems to be the USP.
Cost effective - Unlike traditional archival storage solutions, the “Enterprise data hub is an online system: all data is available for query or compute this not only accelerate Data Preparation and reduces costs of preparing data processing workloads that previously had to run on expensive systems, now can migrate on a commodity hardware build as you grow and even on cloud, where they run at very low cost, in parallel, much faster than before.