Making the most of your data analytics budget

Analytics investment is on the rise, but results can be elusive. Here’s how orgs are spending on tools and personnel to get the biggest bang for their buck.

Making the most of your data analytics budget
Getty Images

Organizations worldwide are spending more on data analytics. The key questions: Are they translating those investments into analytics that actually drive business value, and if not, what can help give them the best results?

A 2018 study by International Data Corp. (IDC) forecasts worldwide revenues for big data and business analytics will reach $260 billion in 2022, with a compound annual growth rate (CAGR) of 12 percent over the 2017 to 2022 forecast period. Revenues were estimated to total $166 billion last year, an increase of 12 percent over 2017.

The industries making the largest investments in business analytics tools are banking, discrete manufacturing, process manufacturing, professional services, and government. Combined, these industries accounted for nearly half of worldwide analytics tech revenues in 2018.

Two of the fastest growing technology categories will be cognitive/artificial intelligence (AI) software platforms (37% CAGR) and non-relational analytic data stores (30%), according to IDC.

Among the biggest analytics trends of late is the emergence of augmented analytics driven by machine learning, according to Gartner. Augmented analytics, which the firm included in its Top 10 Strategic Technology Trends for 2019, uses machine learning to transform how analytics content is developed, consumed and shared. By automating data preparation, insight generation, and insight visualization, augmented analytics will eliminate the need for professional data scientists in many cases, Gartner says.

Automated insights from augmented analytics will also be embedded in enterprise applications, the firm says. Software and services aimed at human resources, finance, sales, marketing, customer service, procurement, and asset management will further incorporate machine learning to help optimize the decisions and actions of all employees, not just those of analysts and data scientists.

Such shifts underscore the thirst for analytics at a time when competition for data science talent is tight. And as vendors further knit machine learning and data science capabilities into the fabric of their platforms, the promise of “citizen data science” — in which users outside the field of statistics and analytics will be able to extract predictive and prescriptive insights from data — will become a reality, Gartner says. Through 2020, the number of citizen data scientists is expected to grow five times faster than the number of expert data scientists.

Here’s a look at how various organizations are spending on analytics, in terms of technologies, partnerships and personnel, and where they are seeing the biggest payback on their investments.

Analytics on campus

Indiana University of Pa. completed a major investment in analytics tools and infrastructure, including a data warehousing platform and business intelligence (BI) tools in 2018 and will continue to implement some of these investments into 2019.

“We have a big focus for analytics on the quality, timeliness, and completeness of the data itself and the quality of the data model,” says Bill Balint, CIO. “We feel this is an area often overlooked in higher education data warehousing/business intelligence.”

The university is increasingly migrating to transaction systems and cloud applications in which data analytics capabilities are built into the functionality, Balint says.

One of the use cases for analytics that will likely provide the best results and return on investment (ROI) is in boosting student enrollments.

“The emphasis on attracting new students and then retaining and graduating those students once they arrive has never been higher,” Balint says. “Analytic result sets that can assist the institution in deploying its resources to help reveal action steps to meet this goal are the most valuable.”

An example would be predictive analytics that could indicate which students are at risk for either not attending the university, not continuing with studies, or failing classes.

“Using these analytics, the university could potentially make business decisions about how to address the need before the student, or any future students in the same situation, ever becomes at risk,” Balint says.

Indiana University of Pa. has three primary areas of emphasis with data analytics, Balint says.

The first is on higher education-specific analytics systems that can help identify the ROI of delivering specific instruction. The second is on predictive modeling and early warning systems that can help identify “at risk” students as early as possible in the process. The third is on new transactional systems that have analytic capability/readiness built directly into the delivered product.

Like many other organizations, the university is eager to find skilled people to work on analytics-related projects.

“Our largest skill gap is in data science,” Balint says. “We are in a rural area and higher education salary structures can also make it tough to attract top talent, especially for an in-demand skill set. This is a top talent challenge looking ahead.” There is always a need for application developers and database administrators who can work in a data warehouse and BI environment, he says.

Data analytics has become a key component for the university, not just in IT but for many different operations.

“We are limited by significant constraints of money, people, and time,” Balint says. “Any tool or concept that can help us get critical and focused information-driven decisions better, faster, and/or cheaper is incredibly valuable in this environment. Analytics have the potential to fit that bill in a big way.”

Analytics has become so important “that the analytical capability and readiness of a transaction system is almost more important than the functionality of that system, which is a huge change from the past,” Balint says.

Modernizing financial records

Artesia General Hospital, which serves communities in Southeastern New Mexico, is in the midst of a digital transformation, and part of that effort involves a growing emphasis on data analytics.

Over the past year, the organization has spent close to 30 percent of its annual IT budget on data analytics, says Eric Jimenez, director of IT at the hospital.

Due to resource limitations, Artesia needed to initially outsource the creation of its analytics program, Jimenez says. As such, the bulk of the analytics budget was allocated toward consulting fees. The rest of the budget went to software and deployment.

“Our main focus was on financials,” Jimenez says. “Our financial team was doing all their reports in Excel. They would gather data from different departments and link them to a master spreadsheet.”

But with Excel there is a limit to the number of users who can edit the spreadsheets, Jimenez says. Also, after many years of collecting data the spreadsheets were becoming large and cumbersome files. The financial department was storing these files on a server, but sometimes managers would take the spreadsheets home on their laptops to work on over the weekend.

“This would cause issues linking back to the master spreadsheet,” Jimenez says. “Another failure was when we had server issues, the Excel files would become corrupt. We would have to restore the file from backups, which would cause the financial team extra work because they needed to gather the data again.”

Now, all financial data is located in one centralized location using an analytics platform, and the financial department is no longer dependent on spreadsheets. “When gathering and analyzing the data [previously], it would take hours to days,” Jimenez says. “Now it is in minutes. The financial users are able to work on other problems and not have to worry with spreadsheets.”

To help gain more business insights from the analytics, Artesia is looking for a data “storyteller,” Jimenez says. “The person should be able to analyze the data and provide a story,” he says. “They should have a high level understanding of process on how the data is collected. Once it's collected, they should have an understanding how the end user is going to use the data.”

Although the hospital is still in the early stages of leveraging data analytics, it is already seeing value. “The time savings has helped us focus on other projects,” Jimenez says. “Analytics is just the first stage and the foundation. Over the next couple months, the organization will be changing to use more analytics in our day-to-day jobs. Once we have a good foundation built, we can start using tools like machine learning and artificial intelligence in our organization.”

Improving business outcomes

Worldwide Assurance for Employees of Public Agencies (WAEPA), a

nonprofit association that provides life insurance and financial service benefits to federal civilian employees, plans to increased spending on data analytics by 15 percent this year.

One of the priorities for the year is creating a business outcome-driven analytics framework, “where we interview the major stakeholder in the organization and based on the outcomes they are trying to achieve, we will create a visual and automate it to the greatest extent possible,” says Brandon Jones, CIO.

Analytics will provide the best results when aimed at the delivery of specific business outcomes, Jones says. WAEPA is aiming to create “a holistic and integrated analytics platform” that can blend disparate functions to support sophisticated use cases, and allow the continuous expansion of functionality and evolution of use cases while reducing implementation complexity and risk.

To bolster its analytics strategy, the organization is looking for IT professionals with experience and skills in data analytics, data warehousing, BI, data science, and big data mining.

WAEPA will tap these skill sets to create data mining architectures, models, and protocols, for statistical reporting, and for data analysis methodologies to identify trends in large data sets.

Analytics could be applied to a variety of areas of the business, including market economics, supply chain, marketing/advertising, and scientific research.

Data analytics is paramount to the organization’s ability to serve customers well, Jones says. “We live in a digital world, and [need to focus] on the customer and how they are evolving and want to consume, purchase, review products,” he says. “Our analytics should inform a multitude of business decisions to increase everything from conversions to personas.”

This story, "Making the most of your data analytics budget" was originally published by CIO.