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The Age of Data

Posted November 16, 2021 in News

Written by Gwen Iarussi, Xpanxion | Director of Test Management

Hand and technology

In case you missed it, the digital economy that was growing for at least the past decade has evolved yet again, and the “Age of Data” is officially in full swing.

Data is at the core of successful businesses these days and computing is driving decision-making at all levels from small companies to Wall Street itself. Forbes reported in 2019 that “computers account for half of all trades and up to 90% during market volatility,” and predicted data is the new currency; a fact easily seen by the continued domination of organizations like Amazon, Google, and Facebook in the data space.

Current research confirms this trend, stating data-driven companies are 58% more likely to hit revenue goals than non-data-driven companies and 162% more likely to significantly outperform them. Gartner predicts nearly 90% of corporate strategies will consider ‘information’ a critical enterprise asset by 2022 and ’data analytics’ as a core competency within successful organizations.

It’s no wonder many companies find themselves scrambling to catch up let alone get ahead of the curve when it comes to their own data. Your company may find itself reeling; trying to make sense of the data you have and then determining the best approach to transform data into a decision-making engine that can drive future success.


Ironically, the challenges surrounding data aren’t in having the data. Most companies have more data than they know what to do with, and sadly, therein lies the problem. With the surge of this “age of data,” companies are collecting more data than ever under the false pretense that, “more is better.” Unfortunately, the reality is that only 32% of enterprise data is ever used and the rest (68%) is discarded, and only 13% of organizations deliver on a data strategy.

To add to this complexity, without a solid data strategy in place, this data quickly becomes larger than the company can realistically manage both operationally as well as financially. As companies are in a race to get more data, they often forget data storage at scale comes with a hefty price tag. On top of data storage cost, teams often take a productivity hit as they are bombarded with the mundane tasks of managing all this data. By the time these costs surface, IT teams are scrambling to justify them as they scrutinize their budgets and measure the returns on this investment.

Another challenge companies face, especially in larger organizations working with enterprise data sets and computing environments, is the impact of “dirty data” or data that is inconsistent, invalid, or missing entirely. Back in 2016, IBM estimated the cost of poor data quality to be somewhere in the range of $3.1 trillion annually; approximately 15% of GDP.


When it comes to tackling the challenge of data quality, it’s absolutely critical for teams to have the clarity and alignment around what they are trying to achieve with their data, assure they do indeed have the right data for the job, and then put best practices in place to ensure the data they need to reach their accomplishments is protected from the point of collection, while it’s being stored, and through all of the necessary steps it must go through to transform into valuable data that can be used by the organization.

Like many problems that we attempt to solve in the quality space, we have to step back and look at the overarching systems in play. If data really is meant to be the engine for business strategy and decision-making, then it’s up to us to ensure we are fueling that engine with the purest fuel and keeping that engine running optimally to assure top performance over time.


This is where a well-defined data quality strategy comes into play. Achieving a high-performing data machine within an organization requires a strong commitment by the organization to take a step back and observe the overarching system as a whole and then identify the most critical areas to focus on to achieve the best outcomes. We do this by answering five key questions:

What are the outcomes we want and need to achieve?

  • This is perhaps the most critical step as it serves as context for prioritizing all follow-up activities and initiatives supporting our data strategy. Identifying the clear outcomes we are striving to achieve lets us have clarity in direction and gain alignment from all stakeholders around how an organization uses its data and the supporting activities.

Do we have the data we need today to achieve those outcomes?

  • This analysis phase lets the organization take an honest look at their data to gain clarity around the state of that data. This process can be incredibly time-consuming and difficult, and it can surface some of the underlying issues with an organization’s data management practices. If done correctly, it enables an organization to move quickly to address their most critical data needs...all while providing a very distinct roadmap to get the organization on track for future success.

How do we effectively use that data to get the right information into the right hands at the right time?

  • This question can be incredibly tricky to answer as you're likely to get different answers depending on who you ask and when you ask them, and yes, everyone has an opinion based on their perspective within the organization. This is where careful navigation and collaboration is key. Working with cross-functional executive leadership to get alignment is most critical. The answer to this question identifies the most important outputs for the data the organization currently has available, and it sets the stage for future efforts. Having clear alignment on what the organization is trying to achieve helps reduce tension and conflict at this phase.

Where are the gaps and how do we fill those as we move forward?

  • It will be evident when pulling back the covers of where we want to be vs. where we are today that we will find gaps in the data we are collecting. Identifying these gaps is a crucial enabler to identifying potential enhancements to our systems or operations that can help an organization take that next step on this path to truly being data-driven.

How do we ensure the integrity of our critical data over time?

  • The answer to this question leads to a scalable data quality strategy that holistically covers your most important data from entry point to all critical outputs, and every step in between. This ensures your data investment is protected over time and as your organization evolves and grows.


As you can imagine, taking on this process is not a small endeavor, and many organizations struggle to prioritize this along with an ever-growing functional backlog, as well as the day-to-day technology operations.

This is where companies like Xpanxion can help. With our team of data quality experts and holistic approach to quality, we can help organizations like yours pave a clear path forward to reach this goal of creating a true data-driven organization.

At Xpanxion, we help organizations derive value from their data and prepare for a successful digital transformation journey. Read our success stories and learn how we’ve helped businesses across verticals ready their data for the future.


About Xpanxion - Solving business problems with technology. We are software product engineering experts with over 20+ years of experience delivering the technologies, software architectures, processes and people critical to delivering success. As a trusted partner, we focus on business solutions and alliances that provide end-to-end value to solving our customer’s problems. We focus on providing best-in-class solutions by developing custom solutions with modern technologies or by delivering industry recognized off the shelf solutions.

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