Self-service analytics key to agile decision-making
With worldwide events such as the COVID-19 pandemic and war in Ukraine causing economic uncertainty, organizations need to be able to make informed decisions in near real time.
Organizations need to be agile amid fast-changing economic conditions, and self-service analytics capabilities enable that dexterity.
Successful self-service analytics, however, takes more than simply investing in an easy-to-use business intelligence platform.
It calls for an organizational investment in data literacy and a cultural commitment to data-driven decision-making, according to a panel of experts speaking during a recent webinar hosted by data management vendor Alation.
The COVID-19 pandemic sparked an unprecedented pace of change, and the war in Ukraine has only added to the economic uncertainty.
Heading into 2020, organizations could get by with quarterly -- or even yearly -- planning, and could use past performance to get a good idea of what was to come. For example, if an organization's sales typically surged during the summer season, or the holiday season, but ebbed at another time of year, the organization could reasonably expect that to happen again.
Meanwhile, if the organization had suppliers who delivered needed goods on a timely basis, it could reasonably expect those suppliers to continue to be reliable.
The pandemic changed all that.
Unemployment surged, eliminating any seasonal sales patterns. And supply chains were obliterated as travel was restricted and unexpected surges of demand for certain goods left shelves empty.
To deal with the extraordinary pace of change, many organizations turned to analytics. Predictive modeling enabled them to play out different scenarios -- worst case, best case and anything in between -- that enabled them to prepare for what changes were to come.
And eventually, some organizations not only survived the tumultuous early days of the pandemic but also thrived as the pandemic progressed.
"If you think about the last two years, it's a perfect example of businesses having to shift their model in an agile, quick way to become more digital and deal with COVID," said Wendy Turner-Williams, chief data officer at Tableau. "Companies, businesses, individuals, analysts, CEOs all need data at the right time to answer the right questions. They need to be agile given whatever is happening in the market."
As a result of the sudden need for digitization driven by the pandemic, chief information officers have been crucial, added Myles Suer, Alation's director of solutions marketing.
"CIOs have kind of been the heroes of the last two years," he said. "They kept things running when they never planned to run things the way it's needed today. COVID really has been an acceleration point."
Now, with oil prices surging and supplies coming out of Eastern Europe disrupted as a result of Russia's attack on Ukraine, there's again economic tumult that data-driven decision-making is enabling organizations to manage.
But it's not just any analytics that leads to agility.
It's real-time -- or near real-time -- decision-making that makes organizations agile, and that means enabling frontline workers and not just those in the executive suite to make decisions that will affect the business.
It means adopting self-service analytics technology and instilling a culture that fosters self-service analytics.
Wendy Turner-WilliamsChief data officer, Tableau
For decades, analytics was the domain of data experts with organizations.
If an employee wanted a report, dashboard or model to inform their decisions, they needed to submit a ticket to an IT team and wait for that data asset to be developed and delivered. Depending on the organization, that process could be a matter of days. But it could also be a matter of weeks or even months.
Pre-pandemic, that might have sufficed.
But amid fast-changing conditions, a report, dashboard or model based on data that's even a few weeks old might be obsolete. Amid fast-changing conditions, employees need to have easy, fast access to data and be able to quickly turn that data into insight.
They need self-service analytics.
"Data used to be just reporting what had happened," said Steve Jones, chief data architect at Capgemini. "Now, we're looking at data driving the business outcome. Self-service is now the business taking control and driving decisions themselves based on data. That's a cultural change from guarded, protected, overly conformed data."
To enable self-service analytics, organizations obviously need the right technology. And most of the most popular BI platforms -- such as Microsoft Power BI, Tableau and Qlik -- enable users to work with data without needing to know code or require only minimal coding skills.
They also enable developers to embed data sets and analytics assets within the everyday workflows -- for example, the CRM and ERP systems -- workers use so they can have data and insights available without having to go look for them.
And many even push insights to employees so they can act and react in near real time as conditions change.
But just as the right technology is critical to self-service analytics, so is the right culture.
An organization that provides employees with tools but doesn't empower them to make decisions is not truly enabling self-service analytics, and is therefore not maximizing its agility.
Similarly, an organization that provides employees with a BI platform but doesn't provide sufficient training in how to use the platform and training in data literacy, which is the ability to derive meaningful insight from data, is not maximizing its agility.
"In order to have a data culture, you have to have data literacy," Turner-Williams said. "There's technology, there's strategy, and there are business processes, but then there's the workforce and the need to educate the workforce when it comes to data in order for them to reap the benefits."
Establishing a data culture, however, is an evolution that takes time, according to Randy Bean, founder and CEO of consulting firm NewVantage Partners.
He noted that only about a quarter of respondents to a NewVantage survey of Fortune 1,000 executives said they had created data-driven organizations, and less than 20% said they had created a data culture.
"I'm a big believer in the importance of culture," Bean said. "Data is an asset that flows across organizations, and to be data-driven, organizations fundamentally have to change the way that they've operated. For organizations that have existed for generations, that's not easy. Becoming data-driven is not just a destination -- it's an ongoing journey."
The necessary tools
In addition to a BI platform designed for self-service analytics, a data catalog is key to enabling employees to make data-driven decisions.
Data catalogs are organized hubs for data sets and assets such as reports, dashboards and models where data users and analysts can search for and find the data they need to do their work, and where data stewards can set parameters on data sets and data assets to ensure the privacy and security of an organization's information.
Those parameters -- essentially, data governance -- serve the dual purpose of keeping organizations compliant with government regulations while at the same time enabling employees to confidently work with data.
"The first need [for self-service analytics] is documenting what the data is and where it is," Jones said. "You have to document the data so people can search for it and find it. And it has to be put in context [because] that's what makes it usable."
Training, of course, is another key to successful self-service analytics.
Citing a study by Forrester Research, Turner-Williams noted that well over 90% of employees were satisfied in their roles if their organizations were investing in data literacy training.
"Investment in literacy is key," she said. "Literacy is a differentiator in the market. It's constantly changing, so if you're not investing in driving the awareness of those changes, then you don't have a culture. You have an implementation event. A culture is ongoing, a journey."
And finally, according to the panel, a decentralized analytics model is needed for successful self-service analytics.
While the data itself should be centralized so it can be governed and managed by data stewards, the analytics -- query, analysis and deriving insights -- needs to be decentralized, freed from that old-style method of submitting requests to a data team and waiting for reports, dashboards and models to be developed.
"We're at a transition point, and the destination is decentralized," Jones said. "The business needs to be in control of the data to drive the competitive advantage. But the future, because data now has value and is driving outcomes, is decentralized because every single decision needs data embedded within it."