People and processes key to a successful analytics strategy
A commitment from the C-suite, including appointing data leaders, that leads to processes that enable data-driven decision-making are critical to successful BI.
A successful analytics strategy depends on aligning people and processes within an organization.
And analytics success has never been more important given the complexities of navigating the uncertain economic climate first created by the COVID-19 pandemic and now continuing as a potential recession looms.
That was the message delivered by during the keynote address of Real Business Intelligence, a virtual conference hosted by the research and consulting firm Dresner Advisory Services.
People and processes are the relationship between an organization's employees and the support they receive from the organization itself. Among organizations that have implemented an analytics strategy, those that give their employees decision-making support tend to report the highest level of success with business intelligence, according to Dresner research.
Nearly 70% of organizations surveyed by the firm that reported having extremely successful analytics strategies also have strong support systems for their employees. Conversely, almost two-thirds of those organizations that say they have unsuccessful analytics strategies also report poor support in decision-making.
Technology also plays a role in analytics success, noted Howard Dresner, founder and president of his eponymous firm. But with the capabilities deemed most important by users evolving slowly and common to virtually all data and analytics platforms -- for example, dashboards, data visualizations and data integration -- technology plays a lesser role in analytics success than enabling people with supportive processes.
Like those supportive processes, technology is instead most effective when deemed a way to support employees.
"Success or failure with things like BI or data and analytics really comes more down to people and processes than technology," Dresner said. "Technology is great enabler. But you need to have the right people in place with the right skills and the right processes."
Meanwhile, Dresner VP and research fellow Jamie Popkin noted that organizations committed to analytics have been more stable over the past few years than those that haven't had success with analytics.
Among organizations surveyed by Dresner, nearly 90% of those that reported being extremely successful or somewhat successful with their analytics strategy also experienced no loss of business or revenue since the onset of the pandemic.
"The work data leaders are doing now has never been more important due to economic conditions," Popkin said.
But while a successful analytics strategy is critical, and alignment of people and processes has a significant impact on the level of success an organization achieves with analytics, that alignment doesn't just happen.
It takes proper leadership, training and tools, all of which combine to provide the support system that helps end users confidently and correctly work with their organization's data.
Sometimes a bottom-up approach is best for implementing new processes and systems. But with analytics, Dresner's research shows that a top-down approach is best, with buy-in at the executive level critical to success.
It's at that level where data leaders can align their organization's analytics strategy with the organization's business strategy. From there, the analytics strategy can flow down through department leaders and eventually to end users.
More than three quarters of organizations with a CDO or CDAO -- or a data leader with a different title -- report having extremely successful or somewhat successful analytics programs. Conversely, among those that report having little or no analytics success, more than half also report having no designated data leaders.
"Identified data leadership is one of the keys to success with business intelligence and data and analytics," Dresner said. "If you have that identified leadership and a charter, an identified reporting structure, and a [data] team, you're going to be more successful than you would otherwise."
Some of the keys to a successful analytics strategy include high data quality, efficient data integration pipelines and strong data governance frameworks. Those are all enabled by processes implemented by data leaders.
Howard Dresner (bottom), speaks with Jamie Popkin (top left) and Chris von Simson during Real Business Intelligence, a virtual conference hosted by Dresner Advisory Services.
Training
Beyond leadership, data literacy is key to a successful analytics strategy.
Just as literacy is the ability to derive information from the written word, data literacy is defined as the ability to derive information from data. Becoming data literate relative to a given organization, however, isn't easy. It takes an understanding of mathematics and statistics mixed with an understanding of the organization's business.
That requires training to support the people making business decisions.
Current research by Dresner shows that there was an increase in the use of analytics tools during 2022. For most of the last decade, numerous studies have shown that only a quarter to a third of employees in most organizations work with analytics tools. Dresner's research showed that as well through 2021.
When combined with high levels of data literacy, however, that penetration jumps over 60%.
"The greater the penetration of users, the greater the data literacy," Dresner said. "It's a virtuous cycle. You want to imbue people with the skills [to work with data], give them the knowledge they need to be fluent with data, and … give them the tools."
Success or failure with things like BI or data and analytics really comes more down to people and processes than technology. Technology is great enabler. But you need to have the right people in place with the right skills and the right processes.
Howard DresnerFounder and president, Dresner Advisory Services
Among the keys to a successful data literacy program are identifying leads who can train and assist business users, developing a clear and compelling case for data literacy that encourages end users to want to become data literate, crafting a blueprint that makes data literacy part of building a data culture rather than a separate skill, and launching with quick wins that demonstrate the importance of data literacy.
"Data literacy boosts every single achievement that's out there," said Chris von Simson, research director at Dresner Advisory Services.
He noted that organizations with high levels of data literacy achieve better operational efficiency and revenue growth than those with low data literacy levels. They also outperform those with low data literacy levels in terms of customer service as well as compliance and risk management.
"You should do it," von Simson said. "It's going to help. And it's not hard to do -- there's a path."
Finding content
A final key to a strong analytics strategy is making data and data products like reports, dashboards and models easy to find and use, frequently with the deployment of a data catalog.
Often, data products are developed, used to inform a specific decision, and then lost among a morass of other data products never to be seen or used again. When a similar decision needs to be made, rather than update and reuse that data product, it's either been forgotten or can't be found, and a similar data product needs to be built.
Not surprisingly, those organizations that organize their data products and make them easy to find have more analytics success, according to Dresner research.
Among organizations that report being completely successful with analytics, nearly 60% say their analytics content is at least relatively easy to find. Meanwhile, among those largely unsuccessful with analytics, about 70% also report that their analytics content is somewhat difficult, difficult or extremely difficult to find.
"It turns out that one of the keys to being successful with business intelligence is if we can find the content," Dresner said. "If we can find those things, we don't have to reproduce them, so we don't have lots of redundancy."
One means of making analytics content easy for users to find is through a data catalog. A data catalog is an index where data products can be stored and organized. In addition, governance frameworks can be put in place in catalogs that put parameters on who has access to potentially sensitive data to ensure that they can safely and confidently work with their organization's data.
"People and [processes] are really important, and they are enabled by technology," Dresner said. "Having the right people, the right skills and the right processes -- and the right way to manage all of that -- is really critical to being successful."
