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5 Critical Data Journey Challenges and How to Tackle Them

The business world is abuzz with the new insights and innovations made possible thanks to the emergence of new technologies like artificial intelligence (AI) and machine learning (ML) or cloud computing. These ultimately promise to increase the pace of new products and services and drive profitability through lower costs or higher revenue. To get there, however, there are several challenges business enterprises must overcome — with breaking down data silos being a key goal. In a presentation for the recent Data Cloud Summit from Google Cloud, I laid out some key approaches to implementing a winning data strategy.

5 Critical Data Journey Challenges and How to Tackle Them

Addressing Common Data Journey Challenges 

The fruits of an effective data journey are tantalizing, but winning them can be a challenge that covers many facets of strategy and processes. Too often, the early promise of a data journey is diminished due to a combination of complex factors that defy easy action and resolution. Some of the most important include:

1. Lacking a Clear Strategy

To maximize results, it pays to take a clear-eyed look at both the current and desired future state of data strategy and make sure it is aligned with business strategy. This is a key step that often goes overlooked. A data strategy that is built on “more insight” or “better innovation” will quickly run into challenges.

To be effective, it is important to recalibrate around existing efforts and calibrate more broadly to meet long-term business objectives — rather than specific “tooling” challenges. Avoid the trend of switching from tool to tool, but rather focus on procuring a set of tools that will help support the organization’s business objectives. Tooling strategy versus business strategy may not always align, and in those cases, the business strategy should take precedence.

2. Leaving Out Key Stakeholders

All data journeys are unique. A one-size-fits-all approach will make data strategies ineffective (at best) and detrimental in most cases.

To address this, it helps to identify the many stakeholders in a solution and understand their needs and motivations as a window into identifying distinct department goals and outcomes. One helpful tool borrowed from marketing and UX development practices is the use of personas. By defining the characteristics, needs, and motivations of stakeholder types, it is possible to find common themes and requirements for data outcomes that, in turn, yield a common data thread that leads to greater insights and better outcomes.

3. Failing to See The Purpose of Certain Datasets

Too often, business leaders fail to recognize the utility of the specific data sets they possess. These datasets often become the “property” of specific departments and are not shared with the broader business. This usually leads to less-than-optimal results.

One handy template for categorizing data is to define it either as a racehorse or a workhorse. Both serve a purpose, but how they are best deployed — and the outcomes they produce — are fundamentally different. Racehorse data is high-powered and capable of producing unique insights. Workhorse data, on the other hand, is less flashy, but it may have higher throughput uses, and it is dependable over the long haul. Recognizing these data types helps to maximize outcomes and optimize performance while ensuring your organization is making the right resource investments.

4. Locking Insights Behind Data Silos

It should come as no surprise that the deepest levels of insight are generated when all of the wheels and gears in an organization are engaged, coordinated, and working together. And to those on the front lines, it will also come as no surprise that such levels of efficiency are difficult to achieve. Further, data analytics emanating from product and marketing analytics to drive business decisions and sales analytics derived from the sales organization are often separate and distinct.

In one recent case, Maven Wave assisted a global retailer to unite their data and, in the process, helped drive real-time data integration between sales and marketing strategies. This level of integration led to the generation of metrics beyond simple “cost per click” to more meaningful insights such as “revenue per click.” This deeper insight is only possible when data silos are eliminated.

5. Going The Journey Alone

When it comes down to it, there is a big gap between wanting meaningful insights and enhanced outcomes from integrated data versus actually getting there. A lot of factors have to be just right: strategy, funding, technology, team, incentives, and data.

To get there, Maven Wave can lead a thoughtful and comprehensive assessment in a series of steps to diagnose problems and solutions, find the early wins needed to build enthusiasm, and ultimately, deliver lasting change and results. 

Getting your data to the cloud is a journey, not a destination, and it helps to have a trusted partner to assist and accelerate the effort by enabling enhanced insights and the likes of AI and ML. To learn more, watch the Atos / Maven Wave presentation from the Google Cloud Data Summit or contact us.


About the Author

Alex Mendoza
Alex Mendoza has been working with data since before data became a buzzword. Alex has 20+ years of experience delivering data-driven analytic solutions, expertise in analytics, and data solution design. He is well versed in working with structured/unstructured, real-time/streaming data, and external and non-traditional data sources. At Maven Wave, Alex concentrates his focus on analytics modernization.
April 7th, 2022

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