The already wide adoption of data analytics will expand further in 2021 with the initial adoption of relatively new information technologies and broader rollouts of more established technologies.

As a CIO or senior IT manager, consider which of these 10 data analytics trends can deliver more value for your organization if you implement it.

Prescriptive analytics will become more common

More organizations will move along the traditional continuum of analytics capabilities toward prescriptive analytics. Prescriptive analytics is software capability that makes recommendations for decisions – it doesn’t just present data or make forecasts.

The benefits of prescriptive analytics include:

  1. Reducing decision-making risks.
  2. Evaluating more scenarios than is practical without this capability.
  3. Enabling data-driven decision-making.
  4. Supporting repeatable, scalable business processes.

Augmented predictive analytics will be embraced

Some organizations will embrace augmented data analytics by incorporating machine learning (ML) functionality into their predictive analytics software.

This amalgamation improves the sophistication and accuracy of forecasts. An excellent place to start is to use readily available ML algorithms rather than develop new algorithms from scratch.

Self-serve data analytics will evolve

Self-serve data analytics accelerates results for the business and takes delivery pressure off the IT department. What’s not to like?

The problem is that the design, construction, operation, and maintenance of a reasonably comprehensive self-serve data analytics environment consumes dollars and staff resources. The incredible software development productivity of data analytics software packages reduces this cost, but it’s still material for many organizations. Worse, some organizations have realized that turning business analysts into software developers is not a good outcome for the effective use of staff or business analysts’ career direction.
These considerations are causing some organizations to make the investment and operate the data analytics environment as a formal part of the IT department, while other organizations are making little investment and are leaving the data analytics environment relatively informal and ad hoc.

Value of data will be recognized

Many organizations’ management has come to recognize data’s value through the spectacularly successful examples of Facebook and Google. In response, they will sponsor more projects to:

  1. Accelerate digital transformation initiatives.
  2. Curate digital data.
  3. Avoid the loss of data.

These projects’ deliverables add value to data analytics because more of the organization’s data will become accessible to data analytics applications.

Graph databases will be implemented

Organizations will move beyond proofs of concept and implement graph databases for production data analytics applications.

The identification of relationships, some of which are well hidden, form the basis of data analytics value. Data analytics applications accessing graph databases can identify relationships in large volumes of data much more quickly than relational databases.

Embedded data analytics will expand

Data analytics work began quite informally and utterly separate from the organization’s formal applications.

Data analytics functionality will become more embedded in formal applications as regular menu items and cease to be viewed as a special, unique or distinct application.

Augmented data preparation will be adopted

Some organizations will adopt augmented data preparation by incorporating machine learning functionality into data preparation routines.

This adoption significantly reduces the high labour cost and tedium that always plagues data preparation.

Augmented data integration will emerge

Augmented data integration will emerge as a useful advance for data integration. It incorporates metadata and machine learning functionality into data integration routines.
Adding these features to data integration:

  1. Significantly reduces the current handcrafting of data integration.
  2. Makes data integration more resilient by addressing small data quality problems that currently create significant data integration difficulties.
  3. Enables more complex data integration than is currently feasible.

Cloud adoption will continue

Organizations will continue to move their data analytics work to the cloud for these benefits:

  1. Near-instant scalability to respond to variable workloads.
  2. High availability to support 24/7/365 operations.
  3. Outsource the management of the computing infrastructure to contain cost and access related expertise.
  4. High performance to reduce elapsed time to insights.

Data marketplaces will grow

Organizations will license more external data from marketplaces for incorporation into their data analytics work.

For many data analytics applications, the ability to blend internal data with external data adds considerable value.

What data analytics capabilities do you expect to advance in 2021? Let us know in the comments below.