It’s often said that data is the backbone of today’s business, as data-driven decision-making enables business leaders to make informed choices quickly to boost revenue, improve productivity, and stay a step ahead of the market.
Enterprise analytics is defined as the use of data, analysis, and exploratory and predictive modeling to drive business strategies and actions. Successful enterprise analytics requires applying the techniques of data management, data engineering, and strategy development, as well as the use of analytics techniques that range from forecasting and simulation to linear programming and optimization.
According to a recent survey of analytics professionals conducted by the business intelligence software vendor MicroStrategy, data analytics is an important part of the business growth and digital transformation strategies for 94 percent of companies around the world. When companies roll out analytics, the survey found, they report benefits such as improved efficiency, faster decision-making, better financial performance, and the ability to identify new revenue sources.
But enterprise analytics is not without its challenges. Companies need to know what data they should be analyzing to ensure that the right employees have the right access to the right data sources to present data visualizations that provide business leaders with the real-time insights they need.
“A lot of firms are still really struggling with how to implement analytics. That’s why there’s so much demand for professionals with analytics experience,” says Thomas Dadmun, a lecturer in the College of Professional Studies at Northeastern University, as well as the founder and Chief Executive Officer of Point Focal, a financial data analytics company.
Download Our Free Guide to Advancing Your Analytics Career
A guide to what you need to know, from the industry’s most popular positions to today’s sought-after data skills.
The Value of Enterprise Analytics Strategy
According to the MicroStrategy survey, developing an enterprise analytics strategy is the most important factor that contributes to the successful use of analytics, followed by gaining executive buy-in and building a data architecture and infrastructure.
An enterprise data analytics strategy should be easy for the company to follow, relevant to the firm’s specific needs, and updated on a regular basis, according to the data analytics vendor SAS Insights. A strategy developed with these needs in mind allows a company to do the following:
- Set priorities for how to use existing data sources, identify gaps in data, and determine where there may be competition for resources among data owners.
- Create an inventory of physical data architecture, which will help identify where there are differing definitions or terms for the same types of data in different datasets.
- Develop a roadmap for implementing new data sources and analytics capabilities and phasing out older, legacy systems as necessary.
- Implement processes to monitor the quality of data, which enables organizations to reduce inconsistencies, redundancies, or gaps within a given dataset.
- Assess the risk associated with sharing or storing particular types of personal, financial, or health-related data.
- Understand the total amount of data an organization has on hand in order to find opportunities to reduce the cost of data processing and storage.
- Assess who owns data sources and is accountable for determining and maintaining data quality.
- Plan for the introduction of more robust and complex analytics capabilities, including both technology and personnel qualified to oversee the process.
5 Main Challenges of Enterprise Analytics
Enterprise analytics brings many benefits to an organization, but the process is not without its challenges. Some companies will face more substantial hurdles than others—especially if they operate in highly regulated industries such as finance or healthcare—but many will have to confront the five challenges below to a certain degree.
1. Inability to Use All Available Data
Many large companies struggle to make the most of the many data sources they have on hand. According to the Harvard Business Review, enterprises only use about 50 percent of their structured data (data captured in a standardized format) to make decisions and less than one percent of unstructured data such as text and media files.
2. Too Much Time Spent Managing Data
Locating data, putting it in the right format, and getting it ready for data visualization or analysis requires a lot of heavy lifting, Harvard Business Review notes. This means that professionals in a data analytics role can spend up to 80 percent of their time searching for and preparing data, leaving little time to conduct the analysis that’s critical for decision-making.
3. The Balance of Risk vs. Reward
Reaping the full benefits of enterprise analytics depends on how well a company can balance the desire for data control (through privacy and governance requirements) with the need for data flexibility (through integrating disparate data sources for real-time decision support). As Harvard Business Review points out, few companies have a true 50/50 split in this regard; trade-offs must be made that account for factors such as the extent to which its industry is regulated, the maturity of its data management strategy, and its data analytics budget.
4. Analysis Not Available to All Employees
According to the MicroStrategy survey, less than half of employees at 56 percent of companies have access to enterprise data and analytics tools. What’s more, there’s a substantial drop-off between leadership and front-line employees—about 80 percent of those in executive or management roles have access to analytics, compared to just 50 percent of all other employees.
5. Disconnect Between Business and Analytics Needs
In many large companies, analytics professionals report to managers and executives on the technology side. This can lead to a disconnect between what the analytics team prioritizes in developing an enterprise analytics strategy and what business leadership priorities, Dadmun says: “If they don’t understand each other, you could be setting yourself up for frustration.”
3 Ways to Stand Out as an Enterprise Analytics Professional
Given the clear value of data analytics as well as the challenges of effectively making analytics available, it’s no surprise that professionals with enterprise analytics skills are in high demand.
The job posting site Indeed lists nearly 30,000 roles that include the term “enterprise analytics” in the job description. The majority of roles are in the IT department and/or focus specifically on data analysis, data architecture, or software development.
In a competitive job market, Dadmun says it’s important for enterprise analytics professionals to differentiate themselves from other job seekers. Here are three recommendations to help you stand out.
Know Where to Start With an Analytics Strategy
Ranging from the least to most complex, there are four common types of enterprise analytics use cases:
- Descriptive analytics summarizes data.
- Diagnostic analytics determines the root cause of a particular event.
- Predictive analytics tries to predict the likelihood of a particular outcome.
- Prescriptive analytics provides recommendations for actions that will lead to the best results.
When considering an analytics strategy, it’s important for organizations to know not just where they want to be but also where they are, Dadmun says. Professionals who are able to assess an organization’s current assets and how they match with their needs will stand out.
Technologies such as artificial intelligence (AI) are alluring for their prescriptive analytics capabilities, but many organizations could benefit from a simpler approach. In addition, organizations may need to start by ensuring that their data is organized and structured properly and that their analytics tools are flexible and customizable, in order to get any benefit from an enterprise analytics initiative.
“Plenty of firms are doing amazing things from AI, but we have seen a big gap where people want to make the switch to AI, but they need the business intelligence and forecasting capabilities before they can go to AI,” Dadmun says. “Going straight to AI is a challenge.”
Hone Your Data Visualization Skills
Data visualization skills are an element of enterprise analytics that can easily be overlooked, Dadmun says. The prevalence of software tools such as Tableau, Domo, and Microsoft Power BI can make it easy to create data visualizations “out of the box,” with little custom design or coding. But using the same charts and graphs as everyone else won’t set your visualizations apart, he notes.
“Enterprise visual design is about making a connection to the business through an image. You’re trying to tell a story, change someone’s behavior, or get someone to take action,” Dadmun emphasizes. “You need to do more than use the rudimentary visualizations available out of the box. You have to spend a fair bit of time working on it because if you do it like everyone else, it doesn’t differentiate you.”
The goal of these types of data visualizations should be to create a design that will resonate with business leaders who don’t have a background in data programming languages such as R or Python, Dadmun adds.
To do this, it’s important to account for how a visualization will be used. Of the many types of data that are available in the database, which variables matter the most to the decision-makers who will be viewing the visualization? What is the key relationship among the variables that needs to be depicted? Remember that the business leaders viewing a data visualization only have a few seconds to look at a chart, interpret it, and take action.
Build Domain-Specific Expertise
Understanding data programming and analytics is an obvious must-have for someone in an enterprise analytics role, but knowledge about a specific industry or other subject is also valuable, Dadmun says.
This knowledge doesn’t need to be deep to be valuable, he adds. For example, a data analyst working in financial services doesn’t need to know how to manage a stock portfolio, but they should be able to look at a dataset and determine whether it reflects volatility in the market. This will help a data analyst know what data to look for and decide how to use that data.
“One of the first things you do when you receive a dataset is to go through the data discovery process,” Dadmun says. “You need to be able to correlate that data in a meaningful way—to construct a signal that can be of use to the portfolio manager.”
Prepare for a Future in Enterprise Analytics
Northeastern University’s Master of Professional Studies in Analytics brings together the principles of data analytics and the development of critical thinking skills that help students align the output of data analysis with common business objectives. By allowing key decision-makers to translate information into recommended actions, data analysts help a business achieve its goals and stay ahead of the market.
Download our ebook to learn more about the program and how it can prepare you for a future as an enterprise analytics professional.