The Role of Big Data and Analytics in Enterprises

The Role of Big Data and Analytics in Enterprises

Imagining an organization or business without a strategy to leverage data is pretty outdated in the 21st century. Data integration has become a vital part of almost every enterprise or organization. So, the question now is not whether data engineering is needed or not, but the level of data dependency and penetration in an organization.

Data culture in large organizations and enterprises are completely changing the game they are in. The world economic forum has predicted that we would be producing 463 exabytes on a daily basis by 2025. To understand the crazy amount of data involved here, 1 exabyte contains 1 billion gigabytes!

This projection is based on the IoT devices’ boom, digital ecosystems, and our increased dependency on smart devices. Almost every other business is in the brand game. Creating brand advocates for a particular product/service requires a lot of backend work.

To enable this transition from a normal product/service provider to a distinguished brand requires great insights on the customers, the target audience, the market weather, the break-even factor, the future of the product/service, innovation, and a lot of other factors.

So, what is common among all these things? Data and Data analytics!

What is “Big Data Analytics”?

Starting from big data and its analysis is probably the best way to move forward to address the challenges of the enterprises.

Big data analytics is nothing but converting the available data- both structured and unstructured, into a piece of contextual and meaningful information or data.

Big data itself is the combination of tons and tons of structured and non-structured data from various datasets. Leveraging big data has become one of the most important goals for many businesses, enterprises, and even governments.

There are various tools that are used to extract information from these heterogeneous pools of data, but one has to understand that big data itself is in some sense a tool.

It is definitely not a one-way ticket for achieving sky-high growth, but combining an effective business strategy along with this extracted information and applying it on the field subjectively, can help achieve the desired results.

Enterprise and Information flow

By definition, enterprises have huge data flow because of the size involved. In such conditions, you can differentiate the data at hand into two types- internal data and external data.

As the structure is huge, the scope for data flow from various sources is high.

Enterprise data management can be broken down into the following pieces.

  • Data architecture
  • Data integration
  • Data quality
  • Data analytics
  • Processing infrastructure
  • Metadata management
  • Master data management
  • Data governance

The crucial part is to segregate data that matter and the ones that don’t. An effective data strategy involves the collection and accumulation of data that matters. An effective data strategy for an organization or enterprise will focus on contextual data regarding their operations.

Read Also – How Healthcare Industries can Leverage Big Data Analytics?

Big Data Analytics and its questions

Big data analytics can be divided into four types.

  • Descriptive analytics
  • Diagnostics analytics
  • Predictive analytics
  • Prescriptive analytics

Many successful enterprises leverage these 4 types of analytics to optimize their different operations.

Many startups and new enterprises follow the footsteps of large enterprises while devising a data strategy for their organization.

The emphasis needs to be on the question that these analytics rises. If not for the questions, then the need for these analytics becomes obsolete. Many entrepreneurial problem-solving methods that revolutionized the market was formed by addressing these questions.

For example, prescriptive analytics will help you provide insights based on the other 3 data. Using this, a strategy can be devised to reach a certain goal.

There are many IT consulting companies that provide data engineering services to different businesses and enterprises. This B2B sector is seeing high demand in data integration and analysis in recent times.

Read Also – Big Data Analytics in Manufacturing Industry

Enterprises challenges and solutions

The data trends and data accumulation are only increasing in the past few years. The steady demand for data scientists and analysts is proof of this trend. One of the biggest challenges for enterprises in regards to the data strategy is data storage. The cloud spending of the enterprises is projected to be higher than the non-cloud spending in the near future.

Enterprises adopting cloud strategy is to future proof their operations and effectively store and accumulate their data. Though this was an obvious transformation that everyone was anticipating, the pandemic has pushed the evolution by a great margin.

It even posed a do or die question for businesses in the sense that those businesses that adapted to the situation and devised a digital transformation strategy were the ones to survive.

The enterprises were already facing critical challenges regardless of the size and nature of their establishment.

  • Balancing between the culture of the enterprise and the people.
  • Creating channels for effective ROIs.
  • Adjusting to the market trends and aligning the vision accordingly.
  • Establishing effective communication channels.
  • To monitor the effect of the enterprise on the environment.
  • To abide by the regulations of a particular region/government body.

All these factors completely changed the data game forever. The Data engineering services provided by the digital transformation companies also changed.

Enterprises were forced to create a digital workspace during this pandemic to keep their revenue intact. Even huge job cuts and cost reductions didn’t make it for the long run.

This confinement made enterprises adopt remote and hybrid work models. In such scenarios, the enterprise that leverages data for decision making- be it prescriptive od predictive or diagnostic, were the ones to minimize or negate the damage.

Enterprises coming up with their own digital ecosystems to effectively collect and accommodate their internal data is probably the smartest move in this situation.

Wrapping this up,

Enterprises that incorporate a data-driven culture are the ones that withstand the test of time and also the ones that are closer to their vision. Data integration can be of great help for enterprises and large business institutions, particularly big data analytics.

Read Also – Machine Learning Vs. Data Science – Key Differences And Similarities You Should Know

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