9 Ways To Tap Into Smart Data-Driven Decisions
Today's real-time business world requires organizations to make smarter decisions faster. The good news: The technology exists to accelerate decision-making. What's holding you back?
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Today's real-time economy requires companies to leverage data faster than ever before. While real-time insights aren't always necessary, the accelerating pace of business is forcing companies to speed time to insights by an order of magnitude or more.
An organization's technological prowess, its existing technology stack, its organizational make-up and agility, its people, and its culture all affect how quickly data-driven insights can be accessed and acted upon.
"Technology is way ahead of culture. All of this stuff is technologically possible," said Brian Hopkins, VP and principal analyst at Forrester Research, in an interview. "Vendors are packaging data and analytics tools and insights platforms to remove a lot of the complexity. The problem is you still have to fund it, you still have to get people to agree with it, and you have to overcome the organizational issues of data. It means data sharing, co-investment, and governance. All of that has always been a problem and it still is."
As companies strive to use their data more strategically, they often face technological and cultural barriers that impact time to insights. For example, the data leveraged well in one department may not be available to other parts of the organization because it's trapped in a closed system, or because company politics are getting in the way.
"Organizations may be using hundreds of different tools, but the tools may not [interoperate] for business reasons. Every vendor wants to be the hub or key, so they usually allow other tools to inject data, but make it hard to get out," said Luca Bonmassar, CTO and cofounder of hiring platform Gild, in an interview. "Companies that are looking to compete with real-time data need to stop for a second and understand what they're trying to achieve."
According to William King, founder and executive chair of healthcare insights-as-a-service provider Zephyr Health, "The power of big data is being able to serve up relevant insights at a relevant point in time. I don't necessarily need to have massive analysis of massive datasets at all points in time."
Once you've reviewed these nine tips for speeding data-driven decisions and time to insights, tell us about your own experiences. Have you applied any of these in your own organization? Are there other best practices that have worked for you? We'd love to hear from you in the comments section below.
It's difficult to accelerate time to insights if the data isn't readily accessible. This age-old problem is being addressed by a number of platform providers who are connecting to data sources and integrating them on the fly.
"When you want to get to your data you have three challenges: You have to move the bits and bytes from one place to another, you need to understand the data, and you need the ability to make changes based on the shifting needs and requirements of the business," said Amir Orad, CEO of business intelligence software provider SiSense, in an interview.
Despite the rise of the "API economy," middleware, and the availability of data connectors, enterprises still have trouble accessing certain types of data.
Different departments and business units may hesitate to share their data because they don't want to lose control of it. Even if it's technologically possible to get at the data, corporate culture or egos may stand in the way.
"People's cultural perspective is 'This is my data, and I don't want to give it to anybody. If I do, I lose control,'" said Brian Hopkins, VP and principal analyst at Forrester Research. Oftentimes, management incentives and employee metrics discourage data sharing among colleagues, he added.
He advised that organizations need to change management incentives so that employees get something for sharing the data they're responsible for. Companies also need an appropriate way to transfer risk and accountability so that the employee who owns the data isn't held liable if another member of the company ends up doing something wrong with the data that was shared, he said.
Truly data-driven organizations operate differently than those that are still relying heavily on experience and intuition. Data-driven organizations have a clear idea of what they want to accomplish with data -- and they are willing to adapt the business as necessary to stay relevant.
"Organizations need a framework to think about data. It's still expensive to collect, analyze, and interpret data, because someone has to translate the data into insight and then another person has to turn that insight into a decision," said Bart Frischknecht, VP of research and customer success at growth strategy software company Vennli, in an interview. "The cycle of knowing what data to ask for, collecting it, and then interpreting what to do with it is an expensive cycle. Companies need to organize themselves in a way that makes that cycle more efficient."
Data visualizations, BI, and analytics solutions have been continually improving their aesthetics so that users can understand and take action on data faster. Different tools are designed for different audiences, but in some cases their UIs or dashboards may be too foreign, technical, or otherwise complicated for some users to understand.
"You really have to simplify complex data. [Otherwise,] I can show someone an analysis, but unless they have 10 years of experience, it's not consumable," said Amir Orad, CEO of BI software provider SiSense. "It needs to be simple, intuitive, interactive, and discoverable."
Rigid on-premises systems and static reporting lack the scalability and flexibility necessary to compete in today's fast-paced business environment. More organizations are embracing Hadoop, big data environments, flexible analytics solutions, and the next-generation versions of enterprise staples, but it isn't always clear what the technology stack should look like.
"There's a lot of exploration going on and perhaps confusion about what the new technologies mean," said Paul Blase, principal and US advisory analytics lead at PricewaterhouseCoopers (PwC), in an interview. He noted that, while some organizations may think of the newer software and platforms as replacements for older technologies, "We don't see that at all."
Instead, said Blase, "We see continued need for very high throughput data environments, for structured data, and the need to integrate it with unstructured data."
More organizations are driving higher levels of value and accomplishing faster decision-making using predictive and prescriptive analytics. For example, executives are using scenario analysis to inform decision-making. In industrial settings such as aircraft maintenance, companies are combining sensor data with notes from the maintenance staff indicating how a problem or issue is resolved.
"You can increase the predictive power of a model that was using structured data [by adding] unstructured data. The IoT and proliferation of sensors and the ability to analyze unstructured data [are] creating a lot more momentum to integrate data and analytics into operational workflows," said Paul Blase, principal and US advisory analytics lead at PwC.
Despite the technological advances, people's habits are a significant obstacle, especially when data-driven insights contradict experience and intuition.
Some departments and lines of business have been using data to optimize processes for decades. However, it's still the rare case that the same information is being used more broadly in the enterprise to increase its strategic impact. An example is connecting marketing data with point-of-service data to gauge how marketing impacts sales, and then using that information to make pricing decisions or to inform product development.
"That integration point across the demand funnel is still difficult," said PwC's Paul Blase. "If the company has a data-driven mindset, it has to figure out how to break down organizational silos where that data is frequently trapped. [It needs to] build common data models and analytic models that the [various] groups can use to create a greater whole, as opposed to each group hoarding its own data to represent their performance in the best light possible."
Data quality is a double-edged sword. If the data quality is poor, it negatively impacts the accuracy of insights and business decisions. However, if the data is expected to be perfect, it can unnecessarily delay insights and action.
"[Data quality] may be used as an excuse [to achieve less] with data. Our experience is that data quality is only relevant in the context of its use," said PwC's Paul Blase.
IoT devices can speed time to insights in industrial settings, in business environments, and in relation to customer experiences, assuming the underlying infrastructure and architecture can support real-time or near real-time operations.
"When you're able to have a machine that tells you it ran out of oil automatically, that's a real-time insight. It's a lot faster than waiting for a human being to discover the information [and then] enter the information into a phone or desktop device indicating that the system needs to be maintained or repaired," said Glenn Johnson, senior VP of software platform provider Magic Software Americas, in an interview.
IoT devices can speed time to insights in industrial settings, in business environments, and in relation to customer experiences, assuming the underlying infrastructure and architecture can support real-time or near real-time operations.
"When you're able to have a machine that tells you it ran out of oil automatically, that's a real-time insight. It's a lot faster than waiting for a human being to discover the information [and then] enter the information into a phone or desktop device indicating that the system needs to be maintained or repaired," said Glenn Johnson, senior VP of software platform provider Magic Software Americas, in an interview.
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