Addressing Key Analytics Challenges

As the analytics market continues to evolve, we see a number of key analytics challenges that have emerged which show important needs among the analytics community.  Let’s look closely at these analytics challenges and ways organizations can solve them.

Breaking down data siloes and delivering insights

A recent IDC InfoBrief found that at companies identified as being “Best-Run” 68% of respondents Strongly Agree that their company is working to eliminate data silos, a critical step to becoming data-driven.  Of the “Laggards”, only 7% strongly agreed that they were actively working to eliminate data siloes.

The same IDC study also showed that 64.3% of the Best-Run companies had a Very Well ability to provide insights across the business, and 100% had a Very Well or Well ability to deliver insights.  Of the laggards, none claimed to have a Very Well ability, while only 16.5% had Well ability.

This shows that breaking down data silos and the effective sharing of insights has a positive correlation with company performance.  In fact the two are also interrelated.  Breaking down data siloes will lead to greater analytic team productivity and the ability to create and share new insights with the business faster.

A Virtual Analytics Hub is the perfect conduit to deliver on these analytics challenges.  It provides greater access to data spread across a disparate landscape and stuck in silos, and offers key collaboration and re-use capabilities to sharing insights and building trust.

Data Management Challenges

A Gartner Data Management Strategy Survey asked users: What factors do you consider to be the most challenging to your data management practices.  This question identified two major challenges in data management:

  1. Finding and identifying data that delivers value (58% of respondents).
  2. Supporting data governance and data security (57% of respondents).

Finding and identifying data that delivers value is a key analytics challenge.  As they are posed with a dizzying number of new analytic questions with increased complexity, analysts and data scientists need to find the right data to best answer the question.

A Virtual Analytics Hub not only helps the analytics community find data faster, but also has collaboration features that share knowledge about the data.  Through this shared knowledge, analysts can find data best suited for their task at hand to produce more comprehensive and accurate results, and can build trust in the data so the business teams can feel confident in using the insights delivered.

Emergence of Enterprise Data Fabric

An Enterprise Data Fabric is a new category of data solution envisioned by Forrester Research.  The Enterprise Data Fabric is designed to address many of the analytics challenges around managing data for analytics.

The Enterprise Data Fabric is an evolution of the Big Data Fabric, become more enterprise-wide and scalable, and is defined by Forrester as a platform that:

Dynamically orchestrating disparate data sources intelligently and securely in a self-service manner, leveraging data platforms such as data lakes, Hadoop, Spark, in-memory, data warehouse, NoSQL and others to deliver integrated and trusted data to support various applications, analytics and other workloads.

The underlying technology (Hadoop, Spark, In-Memory, etc.) is somewhat irrelevant as long as the platform delivers on the key ingredients and can scale.  In fact, the best solution for analytics is a SaaS Enterprise Data Fabric solution that hides the underlying technology and delivers a true self-service platform and tools for the analyst community.

We tend to define an Enterprise Data Fabric as:

A single platform and tools that orchestrates, discovers and catalogs disparate data sources securely to support self-service discovery, integration, sharing and collaboration of data to build knowledge and trust, and deliver the optimal data to analytics and applications.  An EDF should support multiple optimal means of delivering data, have optimized easy to use interfaces for different personas, while supporting a common catalog for collaboration and sharing among the different personas and team members.

By melding multiple delivery methods such as data pipelines and virtual data query into a single solution, with tools that effectively service the different personas (data engineers, data analysts, business analysts and data scientists) into one resource, organizations can more efficiently manage data for the variety of analytic needs and speeds.  The combination of Datameer and Neebo deliver on the promise of a true Enterprise Data Fabric

Collaboration

Collaboration is another key analytics challenge as analytics teams get larger, skills become specialized and knowledge becomes somewhat siloed.  A recent Ventana Research Data and Analytics in the Cloud Benchmark Report highlighted the increasing demand for the use of collaboration in analytics.  In the survey, 38% of organizations were using some form of collaboration in their analytics, with another 29% planning to adopt it within a year and 23% planning to adopt it sometime.  This led Ventana to predict that:

By 2020 collaboration will become a standard feature of ¾’s of data and analytics processes in much the same way visualization is part of analytics today.

Collaboration is now an essential part of analytics to speed the discovery of the most relevant data for the analytic problem at hand, and to build greater trust in both the data being used and the analytics results it produces.

A Virtual Data Hub allows the analytics and data community to easily share what they know about data assets, share and re-use data, and allows multiple team members to work collaboratively on a common analytics problem each bringing their unique skills and knowledge.  These critical ingredients help speed the time to insight, create more comprehensive and actionable results, make the analytics more explainable to the business, and allows business teams to more confidently take action on the results.

Wrap Up

Breaking down data silos to deliver more insights, finding the most relevant data for the problem at hand, orchestrating the delivery of data across the enterprise, and increasing the level of collaboration amongst the analytics community are all key new analytics challenges organizations face today.  Organizations that stare down and overcome these challenges have will be the top performing companies, or as IDC calls them, “Best-Run.”

The Neebo Virtual Analytics Hub is a SaaS solution allowing analytics teams to find, create, collaborate and publish trusted analytics assets in complex hybrid landscapes. Neebo provides unified access across analytics silos, increases use of analytics assets and offers a collaborative environment that furthers data knowledge to build trust and rapidly answer new business questions.  To learn more visit the Neebo website or see Neebo in person by requesting a demo.

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