Big Data, the buzz word that has been around for quite some time now, brings to the table a new edge which companies can’t do without today. The plethora of digital information available today, when aggregated into massive data sets, provides valuable insights which allow organizations and brands to strategize their businesses efficiently. Over the year, Big Data has enormously transformed from collecting and optimizing chunks of data to garnering exogenous and diverse data to further accuracy and analytical relevance
The Big Data Revolution
Ever since economy came into being, mankind began to fathom the importance of analyzing data in order to further drive profits and increase sales. Initially, the focus lied in amassing large amounts of data from all possible sources. To unearth all relevant data and interpret it to assist in the decision-making process was a tedious task. Hundreds of hours went into analyzing markets trends manually.
It is then people realized the importance a uniform platform to contain this large amount of data, and a coherent medium to build valuable insights from it. New technologies emerged over time to fulfill this need. Today, tech giants such as Amazon, Google, Twitter and Facebook fully leverage social media data for their business and marketing operations. Not only tech organizations, but sectors such as media, tourism, and transport are also reaping greater benefits from analyzing consumer data.
The challenge to get to business value
New tech and innovations are sprouting up daily, creating more efficient working infrastructure. However, as new tech is unveiled, businesses grab this opportunity to update their tech. In doing so they often fail to realize that though the new tech may be futuristic, the amount of investment that may go in updating to this new tech can be humongous.
When MapReduce was launched, almost everyone jumped at the opportunity to try it out. No later, Spark came into the industry and people found it intuitive and quickly abandoned MapReduce for Spark. This resulted in the loss of thousands of programming hours as now the codes had to be rewritten.
A typical Big Data technology stack
When managing big data stacks, a minimum of 6-12 different kinds of technologies is required. Everything from storage, computing, data warehouses, and higher-level analytics and let’s not forget data discovery, data prep, data security, data quality and governance, and data visualization. A lot of time and money is invested in updating these technologies and keep them integrated. This is such a humongous task that a lot of organizations simply don’t want to indulge in the integration business, but rather invest their resources in providing actionable insights for their organizations.
Present day businesses are witnessing a shift in cloud-based big data and analytics. Eg. Google Deep Learning doesn’t seem to be offering on-premise anytime soon. There are obvious advantages to using the cloud, but a hybrid environment also means greater challenges in designing and managing the data architecture that connects the data to the cloud with on-premise systems.
With the inception of big data, analytics isn’t only used to gather data that could be used for future decisions; it also helps in getting insights for immediate choices. Big Data analytics is a very broad term; there is no single tech that can fully use the potential of big data. Among the plethora of methods available, the most prominent ones are data management, data mining, Hadoop, in-memory analytics, predictive analysis and text mining.
The pace at which tech changes is simply exhausting, and in our frantic race to keep up with all that is happening around us, we rush to grab a new piece of tech as soon as it is released in the market. The focus is slowly shifting from getting value out of data to being up-to-date with the tech, but we often forget that Big data is only as good as the business value it delivers.
What to consider in the right data management solution?
When it comes to Big Data, the only constant is Change. Listed below are a bunch of elements to look for in a data management platform –
- An end-to-end Solution: A complete data management solution essentially includes data discovery, data integration, data quality, data prep, master data management, data security, and data governance.
- Modularity: If a user invests in a complete platform at once, he/she will be stuck to it simply because of the large investment. All platforms should have a starting point, from where people can take on the data management capabilities comfortable to them.
- Abstraction: An abstraction layer between the development layers and the big data tech should be provided. You should be able to code and have the platform determine the best engine to run the code.
- Hybrid: The platform must be able to manage data wherever it resides, from cloud to on-premises or wherever it resides.
- Intelligence: The platform must be able to accelerate productivity by providing intelligence to make recommendations and automate tasks such as parsing and relating new data for greater understanding.
- Self-service: IT will play a role in delivering data that is ready for business use, but after a point, it makes sense to enable the subject matter experts, the business analysts, to do their own data prep and visualization.
Looking for a data management platforms to deliver greater and faster value? Contact us at Datahut.