Every company needs to innovate their digital tools and tactics. The digital world is about embracing change and taking advantage of the latest technologies and processes to stay ahead. If you started a company in the 1920s, you had nearly 70 years before a company disrupted you -- no real innovation required. Today, a company has about 15 years until disruption. It’s predicted that 40% of today's Fortune 500 companies on the S&P 500 will be out of business in 10 years. The time to disruption is shrinking, and to be successful, companies need to innovate, and quickly.

Today’s rapid pace has forced companies to accelerate their rate of innovation. In the age of digital transformation, the best companies use the latest technology to stay ahead of the competition, and competitors are emerging faster than ever before. Digital transformation encompasses everything from shifting customer service models, creating chatbots and apps, improving talent pipelines, the IoT for field service, and much more. There’s no shortage of opportunities to innovate. And that’s the problem.

Companies need direction on where to invest and when to stop investing.  Analytics was meant to help make those important decisions. For example, analytics can help CIOs and technology decision-makers understand exactly where to apply new digital technologies to improve the customer journey. Yet, nearly two-thirds of analytics projects in 2017 are predicted to fail, and many digital initiatives won’t even have metrics to measure success.

Why analytics is failing to spur digital innovation

The pace of innovation in 2017 requires that organizations waste no time evaluating and consolidating their analytics agenda. However, legacy systems and structures threaten to derail today’s analytics projects that need to move at breakneck speeds. A typical customer analytics project can require over twenty data sources, making the process painfully long and slow. In fact, in a recent report by the Eckerson Group it was estimated that there are about eleven steps to get from data to dashboard.

Wasn’t data visualization meant to help the speed of innovation?

Data visualization -- slicing prepared data, visualizing outliers, exceptions, and trends -- is the icing on the cake of an analytics project, not the solution. Data visualization is only as agile and impactful as the data models that feed it. And according to McKinsey, “data has become the new corporate asset class” -- and if it’s hard to mine it, then it’s hard to use it.

The solution to getting valuable insights quickly is aligning data visualization with data discovery, cataloging, preparation, blending, profiling, enrichment, and modeling so the result is a seamless analytics view -- turning data assets into insight-driven actions as fast and as iteratively as possible without reinventing the wheel each time.

Rethinking the enterprise analytics platform to enable data-driven innovation

It’s not just process that slows companies down, preventing them from innovating quickly, the organizational model for analytics also prevents progress. Many organizations use a combination of spreadsheets, business intelligence tools, reporting built into their ERP, CRM or other apps, plus data visualization and discovery tools. Throw in a few data warehouses or data marts, and that’s the common analytics architecture for most enterprises -- disorganized and ineffective.

But what does a well-functioning modern analytics organization look like?

1.     Embrace and enable self-service. Business analysts, data scientists, and IT want their analytics processes to be local, with the freedom to explore, discover, and change direction as needed. They use data warehouses operated in the cloud and new solutions that unify the process from data discovery, to self-service data prep and blend, and analytic modeling.

2.     Data governance and analytics freedom. Some IT organizations think that a self-service model creates chaos, but that’s not the case. Successful organizations bring IT and analytics teams together through an end-to-end self-service platform. They respect existing governance investments and policies and are seamless and transparent. The most successful organizations have shifted IT away from analytics operations to analytics-enablers, increasing productivity throughout the enterprise.

3.     Moving collaboration to the forefront. When you increase the speed of analytics, it’s important everyone is working together. Data, model, and insight sharing thrive in a collaborative culture that emphasizes communication, reducing redundancy and inconsistency. Becoming a center for data excellence can help, as well as platforms that promote metadata, data reuse, cataloging and analytic asset standardization.

Compressing the analytics cycle

Eckerson Group found that enterprises successful at using analytics to improve the speed of innovation had typically shifted away from a traditional, slow-moving eleven-step process. They didn’t just upgrade their tools, they upgraded their process, which created a simpler, iterative cycle. Their recent research outlined key inflection points along the analytics continuum to speed time-to-analytic-business-value. They found three key areas where the analytics cycle can be compressed: data cataloging, data preparation, and data analytics.

A new analytics process for a new age

It’s a new age for how companies innovate. It’s time the analytics process caught up. With so many digital initiatives competing for resources, analytics is a vital tool that organizations should prioritize. Using analytics means speeding up and compressing the analytics lifecycle while applying organizational best practices to maximize productivity and accuracy through reuse, governance, and collaboration. 

To learn more about streamlining the analytics process, download the full Eckerson Group whitepaper.