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Sisense Team Member
Sisense Team Member

It’s widely acknowledged that using data is key for businesses to stay ahead of their competition. Nevertheless, many companies still aren’t making the most of their data. In a report that Sisense conducted with Harvard Business Review Analytics Services (HBR), we identified a significant gap between companies’ intention to get more value from their data, and the reality of their adoption of effective data solutions.

If data gives organizations such a competitive edge, then why does this gap exist?

It’s because they see intimidating challenges to successfully implementing an effective solution and creating a data-driven culture throughout their organizations. Let’s look at the three biggest challenges, why it’s so important to overcome them, and how you can, using AI-powered analytics.

  1. Increasing data volume, variety, and speed of growth. It can seem overwhelming to get to grips with the enormous amount of data that gets generated every day. Traditional spreadsheets and manual analysis just can’t handle it all effectively. In a world inundated with data, and driven by it, more sophisticated analytics is necessary to enable organizations to discover the most valuable insights that would otherwise stay locked within the data.
  2. A huge gap in skills. This exponential growth in data needs people who know how to make sense of it all. In the HBR report,  61% of respondents say a lack of skills and training is the biggest barrier to improving their organization’s use of analyzed data. So, skilled data professionals are in demand, and the demand outstrips supply. To illustrate this, in 2020, three of the top ten emerging jobs were artificial intelligence specialist, data scientist, and data engineer, according to LinkedIn. Many businesses don’t have enough skilled data professionals and hiring them can be costly.
  3. Extracting valuable insights from “noise”. The more data you have, the harder it can be to understand, and extract relevant insights from the mass of extraneous information. Big data needs powerful computational resources to store and organize it all. And the more data you have, the more likely it will be messy, coming in a larger variety of often incompatible sources, formats and languages that need to be organized, transformed, or consolidated. That’s a lot of complexity or “noise” to cut through, to separate the wheat from the chaff and discover what’s valuable. It can seem particularly challenging, and it’s something that less technically savvy business users prefer to avoid, thereby detrimentally affecting the adoption of effective data analytics solutions.

Make data analysis easier with AI

Data analysis can be tricky, but artificial intelligence can make it smoother and simpler, even for non-technical business users. AI-driven augmented analytics enables users to get faster answers and more actionable insights from their data.

One way AI overcomes the skills gap and helps non-technical users is natural language processing (NLP). NLP enables any user to query data using regular language, rather than using code or poring through thousands of lines on spreadsheets. Now, you can get instant results, simply by asking questions like, “What social network gave us the most sales in Q2?”

Consequently, your data becomes accessible to everyone, however complex it is. Gartner predicts that conversational analytics and NLP will boost the adoption of analytics platforms, and that 50% of analytical queries will be generated via search, NLP, or voice.

AI uncovers hidden insights to forecast outcomes and drive decisions

AI takes BI and analytics to a new level by enabling users not only to identify trends and get insights that might not be immediately visible, but also to forecast outcomes that drive decision-making. This is achieved by infusing AI into your analytics platform 


Platforms like Sisense can provide forecasts based on historical data, so that users can predict outcomes and potential changes, and make decisions accordingly. For instance, entertainment venues can analyze data from previous events to predict which shows will bring in the crowds and make a profit. With this insight, they can identify which shows to stage and whether to schedule additional performances.

Sisense’s forecasting capability also helps analyze how different factors can influence outcomes. An example is if you wanted to check whether the type of device your customers use  (desktop, tablet, or mobile) affects your total e-commerce revenue.

Simply select “device type” as the “explaining variable” and see how it affects your predicted revenue. This analysis, called a “multivariate forecast,” gives you the ability to see what factors affect the desired metric, enabling you the ability to optimize your product or service delivery, or your UI.

Furthermore, this analysis enables you to exclude anomalous activity that could skew your data and distort the accuracy of your insights. For example, you may expect an unusual change in activity over a public holiday, or an emergency period in a particular market.  With Sisense you can exclude such periods from your calculation, so they won’t influence the prediction.


Trends are another effective way to help easily understand the bottom line. When data is “noisy” or volatile, adding a trend line makes it easier for you to see how your data is behaving and what pattern or outcome you’re seeking to identify. Sisense provides several types of trend lines, so you can select the one that best fits the behavior of your data, and you can apply Trends to historical and forecast data.

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Alternatively, you might want to analyze something unusual in your data. Sisense Explanations helps you do this. 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.

Insights for everyone

These capabilities make data analysis more accessible to everyone within any organization. That’s why  AI-powered analytics platforms are the future, why Sisense is empowering users through AI and how we’re transforming businesses.

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.