Data Analysis

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Continual Data Analysis drives the decision making process. Solutions are crafted from mental models, patterns, behaviors, and attitudes, justifying an approach based on evidence rather than intuition.

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Related Mindset:

Data-Driven

Segment:

Idea Generation

Inputs:

Data received from current production data gathering as well as digital landscape trends

Outputs:

Ideas that have arisen through Data Analysis to be considered within the Portfolio and Program segments

An organization cannot adopt the Data-Driven Mindset without having Data Analysis as a core competency and source for continual improvement.

There are many statistics that illustrate what everyone already knows to be true: businesses who have Data Analysis as a core competency outperform those who do not. That is a large part of the reason why the Digital Continuum includes data at the foundational mindset level.

“Data-Driven decisions tend to be better decisions. Companies in the top third of their industry in the use of Data-Driven decision-making were, on average, 5% more productive and 6% more profitable than their competitors. This performance difference remained robust after accounting for the contributions of labor, capital, purchased services, and traditional IT investment.”1

Identifying problems and opportunities using real-time data can drive change to meet customer and business goals. Predictive analytics utilize data, algorithms, and machine learning to assess probabilities against future outcomes. Business can surface products and information based on probability models to enhance and personalize experiences.

Data can move beyond identifying problems to offering immediate solutions as well. Prescriptive analytics inform an experience, using a variety of variables, mathematical models, business rules, constraints, and machine-learning. Common examples include self-driving cars, traffic rerouting, and airline pricing models that all rely on a complex set of variables and factors to optimize results.

In today’s landscape, organizations are using Data Analysis as a disruptive force. From organizations that are using predictive analytics to more intelligently route calls in a call center2 to using sensors and Data Analysis to detect potential failure in oil refineries3, Data Analysis is more than a tool, it is a competitive advantage.

With data providing a competitive advantage in today’s landscape, organizations have a clear mandate to adopt it as a core competency. It is expected that the amount of data ingested by an organization could increase exponentially as organizations adopt new IoT devices at a record page, integrate new commercial data sources, and adopt new data-producing technologies.

Successful implementation of Data Analysis requires both the resources to effectively analyze it and a culture bound to enforce its use.

Common Pitfalls

While many organizations are making positive movement on their data strategy and approach, few have achieved the level of proficiency needed in Data Analysis.

Organizations should work to avoid the following common pitfalls:

  • Incomplete data strategy - Mature organizations must invest in a malleable data strategy that covers data gathering, data storage, as well as an organizational structure which allows for Data Analysis across the entire organization.
  • Siloed data - Many organizations have data that is siloed within specific business units. It is imperative that organizations include a strategy for having centralized Data Analysis to ensure that trends can be identified across business units. This can be particularly difficult in organizations that are continually changing through mergers and acquisitions. A complete data strategy should also include an approach for integrating data should such a scenario occur.

Tools

The tools and platforms for Data Analysis have matured greatly in the past 2-3 years.

While there are a variety of options, some of the most common solutions for analysis are included below:

References