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While most organizations know that data is crucial to having a competitive advantage, only 27% of companies have successfully created a data culture or widely adopted a big data or AI initiative, according to a recent NewVantage Partners survey. In its 2020 report “Top 10 Trends in Data and Analytics,” Gartner predicts that only 10% of companies will be using augmented analytics to their full potential. 

Clearly there’s a huge potential for data and AI to revolutionize a business, so why is there such a large gap in companies successfully adopting such initiatives? 

Companies need to overcome three main challenges to effectively implement analytics:

1. Data volumes and variety are growing exponentially. It’s become impossible for humans to make sense of the huge datasets being created daily just by using spreadsheets and manual analysis. Instead, highly technical, code-driven data analysis is needed to unlock insights from these massive amounts of information.

2. There is a massive skillset gap. While data volumes keep increasing, people with the skills to organize, collect, expose, interpret, and glean insights from data are hard to come by. In fact, LinkedIn has reported that three of the top 10 emerging jobs of 2020 were related to data: artificial intelligence specialist (#1), data scientist (#3), and data engineer (#8). And when these in-demand professionals can be found, they’re expensive hires; consequently, many organizations do not have enough data professionals in their ranks. 

3. It’s difficult to separate signal from noise. Sometimes the data is very noisy, volatile, or hard to understand. When this happens, it’s extremely challenging for users to suss out insights, resulting in a low adoption of analytics by business users.

Better Analytics Through AI

AI makes data analysis easier

While data analysis can be difficult, artificial intelligence is helping organizations make sense of their information. AI-driven augmented analytics can give users faster answers and actionable insights into their business metrics. And fortunately, you don’t even have to be a data scientist or data analyst to glean these insights.

One example of how AI can bridge the skillset gap and help users get answers faster is natural language processing (NLP), which empowers every user to query data using everyday speech. Simply asking questions like, “What social network gave us the most sales in Q2?” yields instantaneous results as opposed to digging through spreadsheets or writing SQL (however fun that may be!). This makes complex data accessible for everyone from the most seasoned BI analyst to those in the C-suite. Gartner predicts that conversational analytics and NLP will boost company-wide adoption of an analytics platform from 32% to 50%, giving everyday users access to the answers they need to make decisions. Furthermore, Gartner predicts that 50% of analytical queries will be generated via search, NLP, or voice.

Uncovering hidden insights with AI 

Infusing AI into their analytics platform has become de rigueur for modern BI companies to assist users in forecasting data, identifying trends, and providing explanations that may not be visible to the naked eye.

Forecast

Having an accurate forecast is key for a business to make decisions. Platforms like Sisense can provide forecasts on historical data, empowering nontechnical users to predict business outcomes and changes to critical metrics, and make decisions based on new insights. For example, a theater can leverage past data to predict revenue and attendance per show — thus powering the decision whether to schedule another performance.

Sisense Forecast also helps business users analyze if and how a certain variable influences the forecast outcome. For example, let’s say you suspect that your users’ digital device type (desktop, tablet, or mobile) has an effect on your total e-commerce revenue. You can check that hypothesis by selecting “device type” as the “explaining variable” and watching how it affects your predicted value of revenue. This analysis, called a “multivariate forecast,” is powerful because it gives your organization visibility into the key factors that affect a desired metric, offering you the ability to make savvy optimizations. 

And what if there were occurrences that don’t represent the “normal” behavior of your metric? This could include unusually low sales due to, say, holidays or COVID-19. Sisense Forecast allows you to exclude historical periods from the calculation, so they won’t influence the prediction.

Trend

Trends are another effective way to help business users easily understand the bottom line. When data is “noisy” or volatile, adding a trend line helps provide a visible insight as to the trajectory of the metric in question. Sisense provides several types of trend lines, so you can select the one that best fits the behavior of your data. Trends can be applied to both historical data and forecast data so you can better understand not just your past results, but also possible futures.

Finally, let’s say you see something interesting in the data — some spike or unusual behavior — what now? The next thing to do is to try to analyze what happened. Luckily, there’s no need to alert your busy neighborhood data analyst to crunch the numbers for you. Sisense Explanations helps you uncover possible reasons. Simply click on a point, and Explanations will analyze dozens of factors — and combinations of factors — to identify the most likely contributors to that change.

Powering insights for everyone

AI-powered analytics platforms are the future, making data analysis more accessible to individuals of all kinds across every organization. Dresner named Sisense an overall leader for business intelligence platforms in its 2020 Wisdom of the Crowds market study. Learn more about how we are empowering users through AI and transforming customers’ businesses in this on-demand webinar.

Inbar Shaham is a senior product manager at Sisense. She has 11 years of experience in product management, having worked for Clarizen, Takadu, and ICQ, among others. 

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