Gain Critical Insights You Can Trust

The goal of any analytics project is simple to understand but hard to achieve: transform business data into insights that characterize, predict, and optimize performance. However, according to Gartner, only 20% of analytic insights will deliver business outcomes. Even among those projects that succeed in producing analytics from the mountains of data in an enterprise, those insights may not be used or be usable.

So, how can an organization ensure a given analytics project discovers meaningful patterns in data to accelerate decision making, support digital transformation initiatives, and provide a competitive advantage?

Create a Strong Governance Structure

Based on CTG’s experience providing data and analytics consulting services, organizations should first establish a governance structure. Governance should include C-suite sponsorship of the organization’s data and analytics initiatives with support from a blended team of business and IT stakeholders who establish priorities, obtain funding, eliminate roadblocks, appoint data owners, and monitor implementation progress. Take the time to ensure that these participants understand analytics, the project goals, and the typical data challenges. Make sure that communication and change management are part of the plan.

Know the Data

Before beginning an analytics project, assess the source data. While there will inevitably be discoveries during the project itself, identifying the roadblocks beforehand creates a more accurate project plan and scope. Most organizations find that their data has one or more of the following challenges:

  • Unstructured data. Is the meaningful information in text or notes fields?
  • Poor data quality. Is the data inconsistent? For example, are temperatures recorded in both Celsius and Fahrenheit? Are there values that do not make sense?
  • Complex data architectures. Is the data in a “big” system that is hard to understand? Is it across systems that are not tied together consistently? Are assumptions needed to make the data meaningful?
  • Stale information. Do end users have confidence in the data being “up to date?”
  • Data privacy and security. Is there sensitive information that cannot be exposed outside the source system?
  • Inconsistent business rules and definitions. Do fields take on more than one meaning depending on the group? Are they created at different times in the workflow with differing degrees of reliability per product or service?
  • Adherence to regulatory requirements. Is the data governed by HIPAA or GDPR, for example?

Knowing your data management needs as soon as possible is vital to project success.

Choose Technology Carefully

Make sure that the technology selected is right-sized for the project. Are there ones that are better than others for the industry? Also, what already-licensed technology is “hiding” in a department? What data warehouses or data lakes exist already?

Involve the End Users Early and Often

Analytics projects can produce significant insights, but if the people asked to change behaviors and practices reject those insights, the project has not succeeded. As soon as possible in a project, show those end-user communities how the data is sourced and used. Building confidence in the data and the resultant analytics must happen throughout the project.

Business Intelligence

The wide variety of products used for data visualization, drill-down analytics, ad hoc queries, predictive modeling, and data mining are collectively known as Business Intelligence (BI) tools. This can result in an executive dashboard that highlights a KPI in red, yellow, or green based on a pre-existing business rule, indicating that a certain condition was met and further investigation might be required. It could also manifest as a hypothesis based on data patterns that was developed via a data mining tool.

Whatever the format may be, the most important thing about an analytics project is that it provides actionable information that allows businesses to change for the better.

What’s on the Horizon

As one of the leading data and analytics consulting companies, we understand that analytics does not exist in a vacuum. Instead, other digital accelerators are adding to its capabilities. Analytics projects must consider how these technologies and methodologies can contribute to their efforts.

  • Test Automation - Automation

    Intelligent Automation

    As workflows are automated, those workflows themselves become a source of data. In addition, artificial intelligence tools that can create structure out of previously unstructured data can open up a variety of data sources. Machine learning can then be used to analyze these new data sources, in addition to historical data, to answer questions and make predictions/decisions to support the business.

  • Devops

    DevOps

    As organizations adopt DevOps, they can also apply this mindset—focused on increased collaboration and agility throughout a project’s entire lifecycle—to data management and analytics. Leveraging DevOps practices when managing data, known as DataOps, can improve data quality, lower costs, reduce complexity and cycle time, ease operationalization, and improve compliance. In turn, access to this trusted, integrated, real-time data for analytics helps businesses make data-driven decisions in support of strategic initiatives.

Accelerate Your Digital Transformation With Data and Analytics

Organizations that can rely on a foundation of accurate data for decision making and digital transformation initiatives will be more successful in achieving their desired business goals.

Whether you need help developing and implementing or optimizing an analytics/BI program, or managing your organization’s data plumbing on a day-to-day basis, CTG’s data and analytics consulting services can help. Our experience providing data management services, information management experts, and best-of-breed technology enables you to unlock the power of your enterprise data.

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