Select Page

Achieving Business Analytics Success

Author: Ron Faggioli | | March 31, 2022


Digital analytics offer enterprises an almost limitless array of values because they are as malleable as each business needs them to be.

Further, these analytical capacities continue to evolve as more companies develop proprietary analytics to meet their specific sector demands.

Organizations are now devising digital analytics algorithms to inform their future strategies as well as keep them apprised of day-to-day activities. Those that also apply directives from their data to operationalize their systems will be at the forefront of their industry.

The Significance of Strategy

Many companies use business models to construct their systems and networks, then maintain those models to retain their market share. However, they can also use their business modeling processes to develop sustained value creation over time, not just maintain the status quo. Your company can retain and grow its value into new economic opportunities by successfully adapting its business models to embrace evolving consumer and industry demands.

Today’s thriving companies are embracing emerging data analytics programs to upgrade their business modeling technology from systems maintenance to value creation. Combining the resources of data analytics, artificial intelligence (AI), and machine learning (ML), their enhanced software is finding relevance and meaning in information buried deep within corporate data lakes. By incorporating these data insights into your organization’s strategies and culture, you can experiment with and explore new ways of configuring operations while also discovering new business opportunities.

Analytics as a Strategy Tool

Successful enterprise leaders often base their corporate strategies on ‘hard’ data, such as market, consumer, and financial information, as well as on ‘soft inputs’ – their ‘gut’ belief or experience-derived intuition.

Data analytics can enhance both those elements by making unexpected connections within data libraries in response to craftily created queries. The resulting suggestions can explain existing situations as well as offer novel steps forward that may not have been contemplated without the technological intervention.

As a strategy tool, then, analytics can help you:

  • reduce decision biases that favor known circumstances by positing possible strategic outcomes before expending assets. Pursuing traditional practices because ‘that’s the way we’ve always done it’ is no longer a viable method of doing business.
  • identify emerging trends through diagramming your current business context and how that relates to unfolding sector dynamics. Knowing where the market is going facilitates early investments in positive responses.
  • analyze complex market factors to anticipate how those will impact your current situation and how your organization can pivot to embrace new developments,
  • inform your organization’s brainstorming capacities by providing real-time data to support new growth ideas or refute suggestions that may prove unprofitable.


Data analytics can power your organizational strategies by parsing and connecting together the billions of data bits stored on your servers in hundreds of different formats and sources. Not only can the programming enhance your decision-making, but it also provides the documentation you need to justify changes to long-established systems.

Analytics as an Operational Tool

Despite their promise as a business building engine, and even with significant investments in the science, research reveals that over 85% of data implementation projects fail to achieve their goals. Further research indicates that only four percent of organizations that have made the effort are actually able to deploy their ML and data analytics models to their production environments. The reasons for the dismal statistics are many:

  • Often the data itself is siloed across many databases that aren’t set up for or accessible by analytics programming. Many enterprises have cobbled together their digital infrastructures over time, without a specific plan to assure coalescence among them all. Going into the system to discover and remediate the gaps is often too expensive and time-consuming to justify the investments.
  • There is not enough transparency between the business teams and the technical teams. In too many companies, these two critical elements use different tools and measurements to determine their ‘success,’ and the misalignment of their efforts inhibits the integration needed to deploy the revised digital infrastructure successfully.
  • There are not enough skilled technicians able to visualize, develop, and deploy the overarching project. LinkedIn previously reported that there were 150,000 unfilled data science jobs across the company, a figure that has surely worsened through the COVID-19 pandemic. Without this critical set of skills on the project, many leaders struggle even to implement their strategies, let alone scale them across the organization.


In short: despite investments in time, talent, and technology, companies are still hard-pressed to demonstrate the full business value of their data analytics projects.

Your organization can avoid the worst-case scenario by pursuing a strategy that eliminates barriers as the process moves forward:

  • Start by thoroughly evaluating your current digital infrastructure to identify which elements will complement the project and which ones will tank it. Legacy soft- or hardware, hold-over manual processes, and data silos are roadblocks to forward progress.
  • Develop a strategy to identify and address these roadblocks that includes the time, talent, and financial resources to remediate each one. This aspect of the project requires its own ‘model management’ to ensure the revisions set up success for the incoming analytics programming.
  • Connect your key business objectives to the analytics adoption project throughout the entire analytics lifecycle – from data collection through modeling through decision-making and finally into operationalization.


Yes, operationalizing the models devised through data analytics is a complex challenge but the analytics themselves reveal the high value of pursuing the project. The data indicate high success for enterprises that use data to develop their corporate strategies and then implement them into winning business operations.

Datavail’s data engineers, analysts and systems management teams can help your organization find its brightest future by deploying business intelligence solutions. Contact us today.

For more information regarding achieving analytics success, download our white paper, “Automated Insight Reports: Data Analysis Applied.”

Oracle BI Publisher (BIP) Tips: Functions, Calculations & More

Check out these BI Publisher tips including functions & calculations so you can understand more about the production and support of BI Publisher reports.

Sherry Milad | January 15, 2018

How to Index a Fact Table – A Best Practice

At the base of any good BI project is a solid data warehouse or data mart.

Christian Screen | March 16, 2010

Art of BI: How to Add Comments in Oracle BI (OBIEE)

Ultimately the goal of commentary in OBIEE is to have a system for persisting feedback, creating a call to action, and recognizing the prolific users.

Christian Screen | December 29, 2013

Subscribe to Our Blog

Never miss a post! Stay up to date with the latest database, application and analytics tips and news. Delivered in a handy bi-weekly update straight to your inbox. You can unsubscribe at any time.

Work with Us

Let’s have a conversation about what you need to succeed and how we can help get you there.


Work for Us

Where do you want to take your career? Explore exciting opportunities to join our team.