Self-service analytics is the key to agile decision-making
Organizations need to be agile in rapidly changing economic conditions, and self-service analytics capabilities enable that dexterity.
However, successful self-service analytics requires more than just investing in an easy-to-use business intelligence platform.
It calls for organizational investment in data literacy and cultural commitment to data-driven decision-making, according to a panel of experts speaking at a recent webinar hosted by data management provider Alation .
The COVID-19 pandemic has triggered an unprecedented pace of change, and the war in Ukraine has only added to economic uncertainty.
As 2020 approaches, organizations could get by with quarterly or even annual planning, and could use past performance to get a good idea of what’s to come. For example, if an organization’s sales have generally increased during the summer season or the holiday season, but have decreased at another time of the year, the organization can reasonably expect this to happen again.
In the meantime, if the organization had suppliers who delivered the necessary goods in a timely manner, it could reasonably expect those suppliers to continue to be reliable.
The pandemic has changed all that.
Unemployment jumped, eliminating any seasonal sales pattern. And supply chains have been shattered as travel has been restricted and unexpected increases in demand for certain products have left shelves empty.
To cope with the extraordinary pace of change, many organizations have turned to analytics. Predictive modeling allowed them to play out different scenarios – worst case, best case, and everything in between – which allowed them to prepare for the changes to come.
And ultimately, 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, this is a perfect example of companies that had to change their model in an agile and rapid way to become more digital and to face COVID,” said Wendy Turner-Williams, chief data officer. at Tableau. “Companies, companies, individuals, analysts, CEOs all need data at the right time to answer the right questions. They need to be agile given everything that’s happening in the market.”
Due to the sudden need for digitization sparked by the pandemic, chief information officers have played a crucial role, added Myles Suer, chief marketing officer of Alation’s solutions.
“CIOs have kind of been the heroes of the last two years,” he said. “They made things work when they never planned to make things work the way it’s needed today. COVID has really been a point of acceleration.”
Now, with oil prices soaring and supplies from Eastern Europe disrupted following Russia’s attack on Ukraine, there is once again an economic tumult that decision-making based on data allows organizations to manage.
But not just any analysis leads to agility.
It’s real-time – or near real-time – decision-making that makes organizations agile, and that means empowering front-line workers and not just those in the executive suite to make decisions that will affect the business. .
This means adopting self-service analytics technology and instilling a culture that promotes self-service analytics.
Wendy Turner-WilliamsChief Data Officer, Tableau
For decades, analytics has been the domain of data experts in organizations.
If an employee wanted a report, dashboard, or model to inform their decisions, they had to submit a ticket to an IT team and wait for that data asset to be developed and delivered. Depending on the organization, this process may take a few days. But it can also be a matter of weeks or even months.
Before the pandemic, that might have been enough.
But in rapidly changing conditions, a report, dashboard, or model based on data that’s a few weeks old can be outdated. In a rapidly changing environment, employees must have easy and fast access to data and be able to quickly transform this data into information.
They need self-service analytics.
“Previously, data just reported what happened,” said Steve Jones, chief data architect at Capgemini. “Now we’re looking at data driving business outcomes. Self-service is now about the business taking control and making decisions based on data. It’s a cultural shift from guarded, protected and overly compliant data.”
To enable self-service analytics, organizations obviously need the right technology. And many of the most popular BI platforms, such as Microsoft Power BI, Tableau, and Qlik, allow users to work with data without needing to know code or requiring only minimal coding skills.
They also allow developers to integrate datasets and analytics assets into day-to-day workflows – for example, CRM and ERP systems – used by workers so they can have data and insights available without having to search for them.
And many even relay information to employees so they can act and react in near real time as conditions change.
But just as the right technology is essential to self-service analytics, so is the right culture.
An organization that provides tools to employees but does not empower them to make decisions does not truly enable self-service analytics and therefore does not maximize its agility.
Similarly, an organization that provides employees with a BI platform but does not provide sufficient training on how to use the platform and data literacy training, which is the ability to derive meaningful insights data, does not maximize its agility.
“To have a data culture, you have to have a data culture,” Turner-Williams said. “There’s technology, there’s strategy, and there’s business processes, but there’s also the workforce and the need to educate the workforce about data so that she reaps 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 1000 leaders said they had created data-driven organizations, and less than 20% said they had created a data culture.
“I strongly believe in the importance of culture,” Bean said. “Data is an asset that flows between organizations, and to be data-driven, organizations must fundamentally change the way they operate. For organizations that have been around for generations, it’s not easy. Becoming data-driven data isn’t just a destination – it’s a continuous journey.”
The tools needed
In addition to a BI platform designed for self-service analytics, a data catalog is essential for enabling employees to make data-driven decisions.
Data catalogs are organized hubs for datasets and assets such as reports, dashboards, and models where data users and analysts can search and find the data they need to do their jobs. , and where data stewards can set parameters on datasets and data assets to ensure the privacy and security of an organization’s information.
These settings — essentially data governance — serve the dual purpose of keeping organizations compliant with government regulations while enabling employees to work confidently with data.
“The first need [for self-service analytics] documents what the data is and where it is,” Jones said. “You need to document data so people can search and find it. And you have to put it in context [because] that’s what makes it usable.”
Training, of course, is another key to self-service analytics success.
Citing a Forrester Research study, Turner-Williams noted that more than 90% of employees were satisfied with their role if their organizations invested in data literacy training.
“Investment in literacy is essential,” she said. “Literacy is a market differentiator. It is constantly changing, so if you don’t invest in raising awareness of these changes, you don’t have a culture. You have an implementation event. A culture is underway, a journey.”
And finally, according to the panel, a decentralized analytics model is necessary for successful self-service analytics.
While the data itself needs to be centralized so that it can be governed and managed by data stewards, the analytics – querying, parsing and extracting information – needs to be decentralized, freed from that old-fashioned way 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. “Business needs to control data to gain competitive advantage. But the future, because data now has value and drives results, is decentralized because every decision requires integrated data.”