HomeData EngineeringData NewsUnderstanding Stalled Data and Analytics Efforts

Understanding Stalled Data and Analytics Efforts

Fortune 1000 firms need to reconsider their spending on data, analytics, and AI. Of sure, businesses need to spend money on these essential business skills and competitive advantages. They need to look closely at their investment strategies and determine whether they are producing the profits and levels of company value that they are aiming for.

Data, analytics, and AI efforts have halted or even regressed, according to responses to a recently published poll of Fortune 1000 and international data and business professionals. Since 2012, when we started to look at how much money firms were spending on data efforts, it has grown to cover additional subjects including analytics, AI and machine learning, the function of the Chief Data Officer, and data ethics. Chief data officers (CDO), chief data and analytics officers (CDAO), and other senior data and business leaders from 116 Fortune 1000 companies and global leaders—in industries as diverse as financial services, retail, consumer packaged goods, health care, life sciences, and others—provided their perspectives in this year’s survey. Unsettling trends were shown in the responses.

Take into account the survey’s findings and consequences from 2023:

  • Only 59.5% of executives reported that their organizations were using data to drive business innovation, which is the same as it was four years ago (59.5%).
  • A disappointing 40.8% of executives, down from 47.6% four years ago, reported that their companies compete on data and analytics.
  • Unsatisfactory 39.5% of executives, down from 46.9% four years ago, reported that their organizations managed data as a commercial asset.
  • Compared to 31% four years earlier, just 23.9% of executives—less than one quarter—reported that their companies have established data-driven organizations.
  • Finally, and perhaps most discouragingly, only 20.6% of executives, or barely one in five, reported that a data culture had been formed across their organizations. This is a reduction of over 50% from the 28.3% of organizations that claimed to have done so in 2019. Not advancement, but regression.

These results are not encouraging. Think about the fact that 83.9% of executives indicated that this investment pattern will continue in 2023 despite 87.8% of executives reporting that their organizations had increased investments in data, analytics, and AI during 2022. Although 91.9% of respondents claimed that this investment is generating quantifiable business value, it appears that this is not enough to affect these crucial organizational transformation KPIs.

What should businesses change in order to have a different result? What do profitable outlier companies do differently? Companies need to invest in data, analytics and AI more wisely and link those efforts to sustainable business growth in light of the impending economic challenges.

Here are some suggestions for any organization that wants to use data, analytics, and AI to revolutionize its operations and reposition itself for the long term.

Understanding the impact of cultural change on business

You must also invest in your culture if you want your technological efforts to be profitable. But this is frequently ignored. It should not come as a surprise that 79.8% of the executives polled believed that cultural hurdles, not technical ones, represented the biggest obstacles to businesses becoming data-driven. Only 1.6% of executives named data literacy as their top investment objective, despite the fact that organizations mentioned investments in commendable technological projects including data modernization, data products, and AI/ML activities.

Education, communication, organizational alignment, business procedures, skill development, training, or any combination of these factors may be the cause of cultural obstacles. For a large organization, change and transformation are never simple, but if businesses are actually serious about reforming their industries and are not just trying to keep up with the competition, they may need to devote more time, energy, and financial resources to this process.

Instead of Boiling the Ocean, Start Small

Data warehousing, master data management, and cloud migration are just a few examples of large-scale technological infrastructure investments that too many businesses make but which don’t yield comparable business benefits. Experience suggests that the best corporations for creating long-lasting data-driven organizations are those that start small, with an emphasis on providing immediate business value and laying a foundation piece by piece.

From the perspective of long-term infrastructure and platforms, investing in modern data environments can be wise. However, if businesses are unable to demonstrate the business value of their data investments at each stage of the process, data leaders run the risk of losing the confidence, commitment, and trust of their customers. The brief and irregular tenures of corporate chief data officers have been a result of this reoccurring pattern for many firms. Data leaders cannot afford unintentional mistakes.

Create solid business alliances and sponsorship at every stage.

Data, analytics, and AI have developed their own specialized language, with phrases like “data mesh” and “data fabrics,” just like any other field of professional specialization. Regardless of the potential benefits of such methods, too frequently these technical phrases sound like opaque jargon that could alienate other corporate executives or foster a lack of trust. This is especially true if expenditures made in these areas fail to deliver immediate business value. Initiatives lose pace and their proponents lose organizational support without a credibility base established on achieving business results. This is a pattern that occurs far too frequently.

Successful data executives communicate in clear, concise, straightforward, and benefits-focused language to blend into the organization. They gain the confidence of their business associates by using terminology associated with business leaders, such as business results, successful outcomes, and customer pleasure. They can use this to find and work with capable company sponsors. Together, they collaborate to create data, analytics, and AI capabilities that result in highly concrete and quantifiable business results that can be directly linked to these capabilities, such as increased customer satisfaction, more successful new products, and entry into new markets. These CDOs and CDAOs have been effective in integrating themselves into the organization’s corporate culture.

Data ethics shouldn’t be overlooked – your customers won’t!

Finally, businesses would be advised to make a significant investment in developing clear policies and procedures that guarantee their organizations’ ethical use of data. An increasing critical mass of pundits is pointing this out as a matter that requires immediate attention because only 40.2% of Executives indicate that their organizations have established data ethics rules in place, and only 23.8% say that the industry has done enough.

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