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Originally Posted: Analytics insight

Today, data is being increasingly seen as a source of competitive advantages for businesses of all sizes today. According to an IDC CXO survey in 2020, 87% of CIOs identified that becoming a more data-driven enterprise was a top priority in the next five years.

With the increasing digitization of business operations and increasing adoption of line of business applications, a massive trail of data is generated on a continuous basis. This is giving rise to several use cases and helping businesses streamline their operations, improve customer service, reinvent their business models and increase their bottom line. But success with data analytics comes few and far between. A few years ago, Gartner came up with a study that over 70% of enterprise business intelligence initiatives fail.

Building a strong data pipeline

Data must be integrated within a powerful analytics architecture in order to deliver benefits and drive optimal use cases. The modern architecture is akin to a water pipeline where the source is spread across and the water goes through several processes. The key to success is then to invest in a strong data pipeline. A strong data pipeline will enable companies to treat their data as an asset and utilize it to drive business outcomes, efficiently and cost-effectively. Treating the data as a pipeline will not only result in improved business insights but will also result in improved data quality and data sanctity – with features such as data cataloging, metadata management, and data lineage for ensuring proper data governance.

The earlier mentioned IDC survey also found that over 60% of organizations are facing difficulties in setting up the right technology stack, assessing the correct value of their data assets and in breaking down data silos to uncover valuable data points, often due to the lack of an effective data cataloging tool.

So, what is the way forward for these enterprises?

Augmented analytics, which marries human ingenuity with machine learning and artificial intelligence is the solution.

Augmented analytics promises to transform the entire enterprise analytics platform in the following ways –

  • Streamlining data discovery, ingestion, analysis, predictions, and interactions between the different data platforms.
  • Enabling easy share-ability and dissemination of results across integrated functions.
  • Automating and democratizing the business intelligence workflow by empowering the people and making it easy to access data-backed decisions, at lower costs.

Augmented analytics will deliver the agility and robustness that is needed to power the modern enterprise analytics workloads and demands. Enterprises need to assimilate external and internal datasets in order to come up with a useful framework that is smart and responsive to the changing business needs and external conditions. Having the right strategy and a well-planned roadmap to move forward is going to be crucial.

A sound enterprise analytics strategy

Below we cover the different steps that organizations need to adopt in order to come up with a sound enterprise analytics strategy –

1. Invest in a modern analytics platform that can address your business needs both in the present and moving forward

2. Get a team – by either hiring an internal team or by onboarding an external partner – who can help you to make the successful transition and move in the right direction according to your requirements and the available resources and budget

3. Ensure that all of your stakeholders – data engineers, architects, data scientists, business analysts, web designers – are aligned around a common strategy

4. Optimize every part of the data analytics process – from data preparation, modelling, visualization and support – in order to come up with a successful data end to end architecture

5. Design a pipeline that is embedded with the latest data architectures and cloud computing technologies that delivers the necessary agility to deliver on the innovations and competitive opportunities

We are inundated with data today. You have probably heard this multiple times. Despite several challenges with enterprise big data management, having a strong data pipeline with different workflows tightly coupled together will go a long way in helping ensure that your enterprise is capitalizing on the advantages offered by big data management.

Author: Chetan Alsisaria – CEO & Co-Founder, Polestar Solutions & Services Pvt Ltd.

An excellent business leader and technologist, Chetan has, over the past 17 years, led many technology-driven business transformation engagements for clients across the globe for Fortune 500 companies, large/mid-size organizations, new-age companies as well as in the government sector.

Chetan’s area of expertise lies in identifying strategic growth areas, forming alliances, building high potential motivated teams and delivering excellence in the areas of data analytics and enterprise performance management.

As Co-Founder & CEO of Polestar Solutions, he has defined the business processes for sales, marketing, human resource, delivery and finance. As a leader, his focus has been on sustainable growth with a win-win situation for all stakeholders (employees, clients, suppliers, society, at large). Prior to founding Polestar Solutions, Chetan worked with leading consulting firms such as PricewaterhouseCoopers, Deloitte and Ernst & Young.

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