PromptCloud is a Data as a Service company that helps businesses harness the power of data. We are a small bunch of people working towards shaping the imminent data-driven future by solving some of its fundamental and toughest challenges. PromptCloud is a Data as a Service company that helps businesses harness the power of data. We are a small bunch of people working towards shaping the imminent data-driven future by solving some of its fundamental and toughest challenges. The PromptCloud experience is about striving to become the best version of ourselves holistically, an experience that lasts a lifetime.

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This is still the #1 question I get from many data warehouse and business intelligence folks. I use to show Figure 1 (BI Analyst vs. Data Scientist Characteristics chart, which shows the different attitudinal approaches for each) and Figure 2 (Business Intelligence vs. Data Science, which shows the different types of questions that each tries to address) in response to this question. However, these slides lack the context required to satisfactorily answer the question – I'm never sure the audience really understands the inherent differences between what a BI analyst does and what a data scientist does. The key is to understand the differences between the BI analyst's and data scientist's goals, tools, techniques and approaches. Figure 3 outlines the high-level analytic process that a typical BI Analyst uses when engaging with the business users.

A semantic layer is a business-friendly representation of data, allowing for explanation of complex business logic in simpler terms. In Business Intelligence (BI), it has been called the metadata layer, semantic model, business view, or BI model. When the semantic layer was first introduced to BI tools 30 years ago, it defined table joins, metric aggregation, user-friendly names and more, allowing BI end-users to simply drag-and-drop fields like Product Name and Sales onto a report. Yes, "no-code" BI has been around for at least 30 years. This allowed early data teams to start thinking more strategically about where to put business logic, but also opened up a lot of complex issues.

Different BI tools offer different approaches to building dashboards. On one end of the spectrum, you have tools that prescribe having one query per chart and on the other end you have tools that espouse implementing a complex semantic layer. I believe there's a middle path that lies between both extremes, and I call it the dataset-centric approach. In the dataset-centric approach, the tool is connected to individual datasets that are expected to contain all of the metrics and dimensions for a given subject area. In this post, I'll describe the strengths and tradeoffs for each of the approaches and make the case for the dataset-centric approach as the ideal one for fast-moving data teams.

Business intelligence is an increasingly well-funded category in the software-as-a-service market. By handling large amounts of data to analyze and benchmark lines of business, BI promises to help identify, develop and otherwise create new revenue opportunities. Pervasive BI remains elusive, but statistics on the category reveal that about a third of employees use BI tools for analytics to inform strategy. The big data and business analytics market could be worth $684 billion by 2030, according to Valuates Reports, if such outrageously high estimates are to be believed. The segment contains too many vendors to count -- a few include Noogata, Fractal Analytics, Tredence, LatentView and Mu Sigma.

The worlds of AI and BI occupy distinct places in the analytics continuum, which is most often understood with concepts like descriptive analytics, predictive analytics, and prescriptive analytics. Users can leverage descriptive analytics and BI tools to explore what happened in the past, while predictive analytics makes use of ML models trained on real-world data to generate an educated guess about what will happen next. However, the lines separating these two camps are getting more blurry by the month. For years, Gartner has talked about how BI tool vendors are adding more ML and AI capabilities to their wares. In its latest Magic Quadrant for Analytics and BI Platforms, the firm talked about how the next generation of "augmented analytic" products will bring ML and AI to bear on things like data prep, query generation, and insight generation.

Augmented analytics, powered by artificial intelligence, will change everything about the analytics and business intelligence processes, by simplifying, improving, or radically changing them. By integrating artificial intelligence and natural language processing elements with traditional BI processes, augmented analytics will transform the insurance end-customer experience by data curation, revealing new insights, and making relevant information easily accessible 24X7. Data is a gold mine that powers the intelligent insurance enterprise. Analytics and business intelligence (BI) act as core enablers for mining both physical assets and digital business opportunities, thus improving accuracy, increasing efficiency, and augmenting the ability of employees to deliver business value. But though insurers continue to collect data, its real potential remains untapped.

Sometimes it is hard to believe that a world before ML existed. So many modern data analyses are built on top of ML techniques and will continue to do so in the foreseeable future. However, not everyone is able to benefit from these vast advances, because using ML techniques mostly involves using Python, developing code, and understanding many new technologies. Especially when Big Data and distributed systems enter the game, things get messy. This is a problem that SQL query engines are trying to solve.

Enterprises have been relying on business intelligence tools like Cognos, Tableau and Power BI for decades now. These types of software revolutionized how businesses generated reports, analyzed them and made crucial decisions (both daily and long term) aimed at improving their market performance. They especially changed the speed at which businesses could generate information and reach these decisions. But in 2020, these tools are beginning to show their age. The primary function of most BI tools is to get business information in front of those who should act upon it. In practice, this means generating reports or providing interactive dashboards that display crucial business data.