We’ve written about the transformative power of data analytics numerous times here at the APEX of Innovation. But with a new year comes new opportunities, as well as new technologies that will continue to evolve in the months ahead.

With that in mind, below are seven key opportunities for enhancing data analytics throughout this year.

  1. IoT Standardization: In December 2020, the federal government passed legislation requiring that IoT contractors provide security that conforms to specific guidelines. As this happens, IoT solution providers will standardize their device security, ultimately driving the standardization of other IoT elements. This will make it easier for organizations to achieve full integration across their IoT networks and pursue additional data analytics initiatives.
  2. Stronger Business Use Cases: Organizations can build stronger use cases by determining pivotal criteria before launching a project. For example, does the use case improve revenue, reduce costs, or deliver on another crucial business KPI?
  3. Purposeful Digital Transformation: Digital transformation has undoubtedly become a media buzzword. But in 2021 and beyond, companies will take digitization to the next level by integrating, analyzing, and deriving actionable insights from their fast-growing stash of digital assets.
  4. Increased Customer Sensitivity: For years, customer-facing organizations have struggled to find the right balance between personalized offers and overly-intrusive communications. With concerns about consumer privacy poised only to grow more significant, it’s important that companies ensure that their use of customer data and analytics to predict customer preferences never crosses the line.
  5. Better Security: Security vendors, government agencies, and other high-profile organizations have all publicly fallen prey to cyberattacks in recent months. In this heightened environment, IT faces renewed urgency to review security and governance measures for all incoming data. In addition, it’s also vital that any third-party data purchased for analytics is examined through the same heightened security lens.
  6. Eliminating Noise: Growth in data sources creates noise, which will only increase as greater volumes and types of data enter the enterprise. Not only can this complicate analytics, but it also causes excessive processing and storage overhead. As such, companies should eliminate extraneous data upfront and reduce data copies after the fact to optimize their environment for analytics success.
  7. Understanding ML, AI, and NLP: As Machine Learning (ML), Artificial Intelligence (AI), and Natural Language Processing (NLP) are increasingly incorporated into solutions, there is a big opportunity for the vendor community to educate customer companies about these technologies and their potential with data analytics.

Check out this TechRepublic piece for more on the above and other opportunities for big data in the year ahead.