Data Is Intimidating. Make Analytics Invisible. – Forbes

As consumers, we reap the benefits of data and analytics in our daily lives all the time. For example, according to a recent Prosper Insights & Analytics survey, 56% of U.S. adults use voice activated assistants – like Siri and Alexa – on a regular or occasional basis. Every single response to a question, every single set of directions, and every single reminder from these assistants is fueled by data. The insights – or answers – are available to us exactly when we need them. The analytics powering it all are working behind the scenes, virtually invisible.

In the business world, however, getting insights from data is not this simple. We caught up with Ashley Kramer, chief product and marketing officer of Sisense, an AI-driven analytics platform provider with over 2,000 global customers, to discuss how embedded analytics can help enterprises overcome analytic adoption barriers – and enable any worker to make smarter, data-driven decisions without thinking twice. 

Gary Drenik: Data is pervasive and part of many things we do today – from helping us drive cars to understanding our residential energy consumption to real-time traffic insights to know when to leave to get to where we’re going. That’s not necessarily the case when it comes to extracting value data in a professional setting, is it?

Ashley Kramer: That’s right. Whether we realize it or not, we all take advantage of data in our personal lives and base decisions off of it many times a day – whether that’s Google Maps pinging us that there’s a faster route available or Spotify recommending a song based on previous plays. The information is there at the right time, in the right context, and does not interrupt our experience.

Extracting value from data in our professional lives is not so easy or seamless, and that’s why there’s still a huge barrier to analytic adoption in enterprises around the world. While the industry has made some progress in recent years with the emergence of self-service platforms that promise to make analytics more accessible to more people, the problem is that insights are delivered via dashboards that require workers to deviate from their daily workflows.

The idea of working with data is intimidating enough for a non-technical person. Having to leave their regular tools and applications – and then analyze a separate analytics dashboard in an attempt to make a data-driven decision – is downright disruptive and inefficient.

In order to make analytics invisible – like in our personal lives – businesses need to take a closer look at leveraging embedded analytics to extend more personalization and customization and enable users to get the answers they need directly within their daily workflows, without having to think about data, analytics, or dashboards at all.

Drenik: Historically, would it be fair to say BI has been about what’s happening now and what happened in the recent past? That doesn’t help us predict what is next and what to do about it. How do you see data science evolving with the future of analytics?

Kramer: Yes. Business intelligence and data science used to be completely separate, but the shortage of data scientists and, again, the availability of self-service platforms have started to blur the lines. The evolution of data science and analytics goes hand-in-hand – now, anyone within an organization can have access to tools that allow them to make smarter decisions with data and leverage things, like predictive analytics, that were once reserved for data scientists only.

The pandemic forced nearly every company to become a digital business overnight, and with more people online using different tools in different places, the future of analytics is providing intelligence to anyone, regardless of title or skillset, at the moment they need it and without disruption. That’s why many innovative organizations are starting to leverage technologies like Sisense to extend analytics directly into users’ workflows (think: directly within Slack, Google Sheets, etc.). Now, instead of wasting time jumping between where the data resides (in dashboards) to where work is done, users can get actions and insights in the same place.

This switch to embedded analytics will help drive adoption and improve outcomes, especially in the age of remote work. A recent Prosper Insights & Analytics survey found that 70.9% of U.S. adults who previously worked from an office hope to continue working from home even after the pandemic ends. Organizations are using more business apps than ever to enable employees to work effectively from home (an average of 88, according to Okta) – and embedded analytics is becoming the only way to stay data-driven without disrupting the workflow.

Drenik: Descriptive analytics is just one piece of the puzzle, what’s next?

Kramer: Descriptive analytics is really the most basic level of insight, allowing organizations to make straightforward calculations based on things that have already happened. Predictive analytics takes things a step further by providing actionable intelligence that forecasts likely outcomes. Prescriptive analytics, the most advanced use case, recommends ideal actions to meet desired outcomes. All play an equally important role in the decision-making process – for example, prescriptive analytics gathers data from descriptive and predictive sources for its models – but, as AI and embedded analytics become more pervasive across the enterprise, end-users won’t have to know whether it’s descriptive, predictive, or prescriptive analytics fueling their insights. Again, the goal is for analytics to become invisible.

Drenik: Can you provide some real-world examples of how Sisense and its customers are successfully infusing actionable analytics in their applications used across the world?

Kramer: We’ve talked a lot about the benefits of embedding analytics into the apps that workers use on a daily basis, so first I will mention that Sisense recently launched several infusion applications that deliver insights directly within Slack, Salesforce, Google Sheets, Google Slides and Google Chrome. Slack users, for example, can type queries into the Slackbot to pull basic charts and records (e.g., “show me accounts that industry = healthcare”) – or on Salesforce, reps can be automatically notified that they’re behind or ahead of their revenue targets.

Thousands of companies around the world use Sisense to embed analytics in both customer- and employee-facing applications and workflows. Two examples include: 

UiPath, a leading robotic process automation (RPA) company, embeds Sisense in their “UiPath Insights” app, which provides analytics for thousands of their customers. Their customers can now measure, report, and align RPA operations with strategic business outcomes, allowing business process owners to personalize and customize process KPIs that measure the value and impact of their overall automation strategy.

Air Canada, the largest airline of Canada, serving more than 210 airports on six continents, uses Sisense in their safety and security department to deliver actionable data to their front-line employees, empowering them to make immediate decisions to improve safety in real-time for both passengers and staff. Air Canada leverages the AI-driven analytics of Sisense into their corporate safety process, for example to replace an airplane part before it fails. 

Drenik: With so many analytics tools on the market, what’s Sisense’s secret sauce that sets you apart from the rest?

Kramer: Sisense goes beyond the dashboard to allow users to build custom analytic experiences and embed them in their customer and employee-facing apps and workflows – making analytics accessible and approachable to everyone regardless of their skill set. Because our platform can infuse intelligence into any area of the business, the possibilities for data-driven impact are endless – whether that means driving more value to customers, optimizing internal business processes, innovating new products and revenue streams. We’ve made it possible for anyone within an organization and their customers to leverage analytics in a way that’s just as seamless as choosing a recommended show on Netflix. 

Drenik: Thanks Ashley, for your insights on how embedded analytics will drive adoption across the enterprise.

Source : From the Web