Data ROI: Challenges and Opportunities

Data ROI: Challenges and Opportunities
Data ROI: Challenges and Opportunities

With ever-increasing costs, enterprises would be wise to make the most out of the data and analytics tools and systems that they already have. Current spending on data and analytics must appreciably contribute to the bottom line in the short term so that enterprises can better weather today’s turbulent conditions.

The cost of doing business is rising across the globe. As economies began to recover from the pandemic, the demand for goods and services rose dramatically. Consequently, businesses had to increase prices to be able to resolve shutdown-related supply chain issues and hedge against their exposure to volatile commodities pricing.

Energy costs rose, too, in 2022, in large part due to how Russia’s attack on Ukraine affected fuel supplies. While the World Bank projected an 11% decline in energy price in 2023 back in October of 2022, this remains to be seen, considering the continued conflict and the rising global demand for electricity.

Climate change is also driving up costs on multiple fronts. Climate crises such as droughts decimate crops, more forceful rainstorms destroy infrastructure, and heatwaves force factories to suspend production in order to have energy allocated for the cooling of residential homes.

There’s also the pressure on businesses to become more sustainable, making them spend on regulations compliance, disclosure efforts, and sustainability ratings and certifications. Enterprises must also reconfigure their supply chains. Efforts to reduce carbon emissions help shorten and simplify shipping and logistics, which decrease costs. However, businesses that used to employ low-wage labor markets offshore are now shifting to onshoring to better maintain working conditions and uphold workers’ rights.

So what do all of these mean for enterprises using data and analytics solutions? Enterprises must optimize data costs and have their analytics produce more business value. Unfortunately, both are easier said than done because of these challenges.


Data accumulation far exceeds insight distillation

Enterprises now have gargantuan stores of data that are only getting bigger over time. According to Fortune Business Insights, the global market size for data storage was US$217.02 billion in 2022. This is projected to grow to US$247.32 in 2023, then to US$777.98 billion in 2030.

However, storing data is one thing, and harnessing it is another. According to IDC, more than 60% of enterprises find it challenging to:

  • Invest in technology to create data.

  • Assess the value of data.

  • Identify valuable data sources.

And even before data can be processed so that it’s ready for analytics, organizations might find it difficult to:

  • Ensure data quality.

  • Capture all relevant data.

  • Classify data.

  • Secure data at rest and in transit.

  • Transfer data in a timely manner.

  • Obtain the context behind the data.

These challenges can prevent a business from fully utilizing the data that’s available to them. Deriving relevant insights can be slow and difficult — unless enterprises utilize powerful analytics tools to process vast amounts of data.

It is therefore no wonder that the global business intelligence (BI) market is projected to grow to US$54.27 billion by 2030. Furthermore, the latest generations of generative AI are poised to make it much easier for enterprises to process vast amounts of data and generate insights that are easy to understand and take immediate action upon.


Data and analytics tools still require highly specialized skills

Many knowledge workers know how to use productivity tools such as text editors and spreadsheet programs, but they often lack the knowledge and skills to use data and analytics tools. The ones who do know how to use those tools, such as data analysts and data scientists, are in high demand. In fact, according to the US Bureau of Labor Statistics, employment openings for operations research analysts will grow to 128,300 in 2031.

To enable staff of all levels to use data and have it inform their actions is to democratize data. To achieve this, enterprises must increase data literacy within their ranks via training programs. There are, however, some obstacles:

  • Resistance to change: Employees might already be comfortable with how they’re doing things, and incorporating data and analytics into their processes can seem to be too drastic a change to make. Moreover, they might feel that dealing with data and analytics is not their job, but the data analysts’.

  • Time, effort, and funding: Training programs take employees’ time and energy away from their primary tasks, so managers might fear that productivity would falter. Furthermore, leaders within the company might not see the value of upskilling staff and therefore refrain from funding or supporting training programs.

  • Not being set up for success: The enterprise must first ascertain the purpose of data and analytics and how this aligns with business goals. Training programs must support the digital transformation of the business. For instance, the analytics tools they’ll be trained to use must match the requirements of the user so that analyzing data is as seamless as possible. Furthermore, they ought to use the least number of platforms, so the training load should be minimized and put less pressure on trainees.


The ROI from analytics substantially outweighs the costs

If harnessing data and analytics is so challenging, then why do it in the first place? The answer is simple: It presents a host of invaluable opportunities to enterprises.

According to IDC research, enterprises with strong data cultures — those that are able to have data at the right time, in the proper form, and with the necessary context — enjoy these positive business outcomes:

  • Increased operational efficiency, revenue, and profit: Another IDC global study covering 1,200 organizations revealed that:

    • 76% claimed improvements in operational efficiency that averaged to 17%.

    • 75% claimed revenue increases that averaged to 17%.

    • 74% claimed profit increases that averaged to 17%.

  • Faster speed to market: 67% of survey respondents from Japan claimed that improvements in data management and analytics resulted in shorter time to market for new products and services.

  • Increases in employee retention. Firms from Singapore, and Australia, and Japan report employee retention increases by 14%, 17%, and 26%, respectively.


IDC’s 2022 global survey  reveals how data and analytics best practices lead to higher returns from analytics investments:

Data and analytics best practice

Percentage of high ROI performers who claim to do the best practice Percentage of organizations without positive ROI who claim to do the best practice
Let data workers access data easily. 82% 64%
Define data access policies well. 59% 49%
Have a substantial percentage of knowledge workers (at least 25%) actively use analytics software other than spreadsheets. 68% 56%
Utilize the analytics skills of the workforce well. 48% 36%
Enable data and analytics workers to collaborate well with one another. 63% 51%
Integrate every step of the analytics process into a single platform. 77% 66%
Automate the data preparation process. 67% 56%


Integrating data and analytics into business operations has become essential

In business, to make data-driven decisions is akin to using proximity sensors in cars — there’s simply less guesswork involved. By using data and analytics tools, relationships between causes and outcomes are easier to find and ultimately manage.

To illustrate, factories can now be outfitted with internet-of-things (IoT) sensors that track the performance of machines and signal when these machines require preventive maintenance. This is way better than waiting for machines to break down and cause costly production delays, but using such sensors also means installing Wi-Fi networks, monitoring dashboards, and implementing a repair and maintenance system for the sensors themselves.

More and more businesses are seeing the value in integrating data and analytics into business operations. According to Gartner, over 33% of large enterprises by 2023 will have data analysts rethink and optimize decision-making processes by using decision intelligence techniques, such as the following:

  • Simulation

  • Decision modeling

  • Statistics and optimization modeling

  • Systems modeling

  • Portfolio analysis

Another survey by Gartner highlights the increasing importance of data and analytics by determining the following predictions:

  • By 2024, 3 out of 4 organizations will use multiple data hubs to enable the sharing and governance of mission-critical data and analytics.

  • By 2024, enterprises that activate metadata to enrich data and weave a dynamic data fabric will shorten the time to deliver integrated data by 50% and increase the productivity of their data teams by 20%.

  • Through 2024, half of organizations will utilize modern data quality solutions to bolster support for digital business initiatives.

  • Through 2025, 4 out of 5 enterprises that do not implement a modern approach to data and analytics governance will fail to scale their digital businesses.

  • By 2025, 4 out of 5 data and analytics governance initiatives that concentrate on business outcomes will be considered essential.

All of these go to show that enterprises must maximize their use of analytics to reap its benefits. In our next post, we’ll look at the concrete ways in which enterprises can fulfill the promise of data and analytics.

Read more articles on Data ROI:

  Data ROI: How to Optimize Data Spend
  Data ROI: Maximizing Value From Data and Analytics

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