Big Data Analytics

With the data explosion happening to the tune of over 2.5 quintillion bytes every day, businesses are finding it increasingly difficult to realize the full potential of these diverse big data sets and uncover actionable insights and trends.

Earlier, businesses would take weeks or months to make a strategic decision using data analytics. But, Big Data Analytics brings a host of new benefits to the table. It uses advanced analytic techniques and a set of elaborate processes to examine large and varied arrays of data to uncover hidden patterns and coorelations, and glean meaningful insights for making smarter business decisions and preventing fraudulent activities.

Big data analytics, in fact, bagged the top spot in the list of business intelligence solutions in 2020, and has been growing in acceptance and importance in the first half of 2021 too.

It’s being used proactively across many key sectors like healthcare, retail, cyber security, ecommerce, media, etc., for a variety of purposes ranging from marketplace analysis, supply chain management and competitive intelligence to gauge customer preferences and identify trends.

Why is the Business Case of Big Data Analytics Improving?

Big data analytics has emerged as a critical trend to watch and significant contributor to income production or cost control.

It’s easy to make a solid business case for real-time operational insights, when a short window of downtime accrues to losses in millions.

Interestingly, big data analytics analyzes the huge unstructured data sets, generated from different sources quantitatively, and helps enterprises anticipate the future to act proactively rather than be reactive after the event has taken place. Needless to say, this is a pivotal factor in maximizing the return on investment (ROI).

Many enterprises, especially in the financial operations sector, are leveraging big data analytics to find better alternatives for multiplying revenues. Better operational insights, better operational risk management!

For sectors that depend largely on customer insights for their success, big data analytics pays off with strategic operational intelligence by taming volume and variety of unstructured data that gets generated from their websites, social media, contact centers, etc. to optimize pricing and lifetime customer value.

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Combining CRM, BI & Big Data for Real Time Answers

Until recently, enterprise CRM solutions, business intelligence (BI), and big data solutions were separate tool sets, often siloed within enterprise IT. Now, more and more companies are realizing that relying too much on big data analytics risks losing the personal approach to selling.

Big Data’s Big Challenges

The term “big data”, originally described by Doug Laney of Mega Group, is widely accepted to represent data sets where the three Vs—volume, velocity and variety—present specific challenges in managing these data sets.

Big Data in Digital Transformation

As retailers brace for the next stage of evolution of the industry, they have realized how critical data is. In their bid to transform their operations and make better decisions, they are investing heavily in big data analytics solutions. However, these investments need to be made carefully.

Best Idea driven by the Right Data is the Winning Formula

Many enterprises are now investing heavily in big data analytics. But, is that worth it?

The answer is – yes.

Undeniably, its significance becomes more evident with increased investments. Its most valued impact is observed in:

  • Quicker decision making
  • Improving sales ROI
  • Identifying opportunities to innovate
  • Predicting the indicators of financial performance
  • Risk management
  • Building an in-depth understanding of financial drivers
  • Product innovations
  • Improving customer experience

Barriers to Big Data Analytics Adoption

While big data analytics is gaining momentum, its widespread adoption is impacted by a few barriers. It’s not uncommon to learn that big data analytics efforts fail before they get big in most organizations as their executive team fails to create the right data fabric to unify various databases within and outside their businesses.

Below are a few common barriers to the big data analytics adoption:

  • Process silos: Many businesses grapple with the collection, assortment, and analysis of data leading to failure in distilling actionable intelligence from it.
  • Poor data quality: The inferior quality of data, collected from disparate systems and processes resulting in unsubstantial analysis, is another major roadblock.
  • Lack of skills: Today, most organizations lack the skill set needed for data analytics, and that the skill gap is decelerating their digital transformation journey.

The Different Categories of Big Data Analytics Services

Broadly speaking, the big data analytics services fall into four major categories:

  • Big Data Analytics: This includes data analytics, strategy and roadmap definition, prototyping and tool evaluation, real-time ingestion processing, scalable data processing and storage, industry-specific KPI toolkits, innovative solution accelerators, big data competency centers, performance benchmarking, product evaluation and piloting, data preparation and ingestion, statistical modeling, development of intelligent algorithms, generation and deployment of insights.
  • Enterprise Data Management: This includes data governance, master data management, quality assessment, architecture assessment, data integration, metadata management, and data security.
  • BI & Data Visualization: This includes reports, alerts, and dashboards generation, development of quality assurance (QA) processes, performance tuning, mobile capability for BI tools, BI platform rationalization, data discovery, visual querying, and benchmarking-as-a-service.
  • Predictive Modeling: This includes data mining, data modeling, statistical assessments, supervised machine learning algorithm development, decision analysis and optimization, and transaction profiling.

The Way Forward

It’s predicted that the potential of big data analytics will only increase over the next few years as more and more enterprises are now beginning to understand that data powers continuous experimentation, radical customization and innovative business models. These practical applications of big data analytics are a key to help businesses gain a sustainable competitive advantage.

However, its enterprise-wide adoption depends largely on some actions that go way beyond deploying the tools:

  • Change Management: The frontline doubts can be eliminated by the democratization of new analytics tools and orchestration of a shift in mindset that would lead to a radical change in its operational approach using analytics. It can’t be done overnight.
  • Cultural Shift: The organizations would need to motivate critical employees for building a data-driven culture adopt through varied innovative approaches like gamification, boot camps, or elaborate employee communication programs.
  • Re-engineer Jobs: Enterprises would need to redefine some jobs by partially automating them leading to a permanent change in some job role. It’s time-consuming and needs intense focus.

Leverage your data for high-impact use cases and drive growth.