3 Ways Advanced Analytics Tech Resolves Business Challenges

In the scope of analytics, AI is a disruptive technology that’s leading the direction of modern business intelligence (BI) platforms. As BI develops, companies are also investing in their data— from storage in data lakes and warehouses, to systems that clean and structure data, to the roles and functions that support the data analytics pipeline.

One of the latest developments in BI is advanced analytics, or AI-driven platforms that autonomously produce data insights at a greater speed and scale than traditional data analytics. Why are advanced analytics so important right now?

Many business leaders are operating off of data insights that are “good enough” based on the time and labor constraints that limit their depth. In tandem with this data are hunches, gut feelings, and other anecdotal experiences that help inform decisions.

These senses can’t compete against truly data-driven decision-making, especially in competitive markets backed by large sources of syndicated data.

The data analysis brought by AI and machine learning is more than a competitive edge. It’s a solution for common business challenges and problems that are actively hindering growth.

With strategically implemented advanced analytics, businesses can turn data analysis into a revenue-generating investment.

Let’s talk about these solutions in more detail, using AnswerRocket’s advanced analytics platform as an example.

1. Advanced analytics converts insights bottlenecks into insights factories.

Traditional data analytics are often bogged down in cycle of routine reports. Additionally, data analytics software is often technical in nature, revolving around dashboard displays that are created by data analysts and consumed by business people.

This means that business people who want to make data-driven decisions must go through an analyst to find the answers to their questions. This process slows down momentum and actively discourages business people from asking every question and exploring every avenue.

When data analysts produce insights for business people, business people must weigh the pros and cons of follow-ups, where the “cons” can be hours or even days of time spent waiting instead of acting. Similarly, analysts, operating against the priorities of business people, may not have the time to produce insights that are as thoroughly researched as they would be in ideal circumstances.

Plus, the process of manually pulling data from different sources, compiling this data, and filtering it on repeat is time-consuming. When each hypothesis or potential answer is buried in the labor of wrangling data, data analysts simply don’t have the time to test their theories to their full extent.

When each question adds considerable time to the production of deliverables, getting deep in the weeds of data can be a disadvantage. Waiting on reports is the kind of red tape that not only slows projects but discourages an iterative, agile approach.

Advanced analytics changes this dynamic. Advanced analytics solutions, like AnswerRocket, automate the production of meaningful insights, eliminating the human element of compiling and analyzing data.

With the power of AI and machine learning, advanced analytics platforms can research an entire data warehouse exhaustively and intelligently; AI knows what data to look at, where to find it, and how to explain it.

“AI knows what data to look at, where to find it, and how to explain it.”

With the computational power of machines, AI can complete this entire process in seconds.

This fast, deliberate, and comprehensive analysis enables the concept of an insights factory.

The insights factory is a centralized approach to data in which different data sources are integrated for analysis through standardized processes and tools. The machine (in this case, the AI) pulls the data together and automates the production of insights.

When targeted to specific, high-priority use cases, insights factories can deliver quick wins by pointing users toward the data that’s most relevant to their roles and functions. Just as raw materials are turned into unique products, the factory packages data based on market-specific customer and shopper insights.

In other words, insights factories make the data work for the business in a streamlined, efficient way that’s driven by the power of machines.

Learn how advanced analytics tech creates an insights factory.

As such, business people can get faster, more comprehensive insights for every question and follow-up they have. Advanced analytics dismantles the traditional division between business people and data analytics.

Let’s discuss this more in the next section.

2. Advanced analytics enables effective utilization of different roles and teams.

Because advanced analytics automates data analysis, these tools are (or should be) self-service. Self-service analytics enable business people to use the tools directly, without having to go through data analysts (a positive for both roles).

Self-service analytics leverages natural language technology so that business people can ask questions as they would normally in everyday language.

For business people, this means they can get the information they need on their own with instant answers to their questions.

Time that would be spent waiting for a report or acting on hunches can instead be spent on the work with the most value. After all, when your analytics can tell you exactly which metrics impact your KPIs, it’s much easier to prioritize work accordingly.

In a broad sense, advanced analytics allows people to put their time and energy toward the kind of creative, interpretive work that’s currently outside the scope of machines.

Business people can focus on:

  • Making decisions. With data at their fingertips, business people can quickly determine what to focus on, with metrics that clearly outline potential growth and/or opportunity in hard numbers.
  • Taking action. Once business people know what they need to do, they can start creating campaigns, adjusting sales pitches, and strategizing based on their data. The ability to spend more time doing ultimately supports innovation. Often, it’s not until we put something concretely on paper that we realize the scope of a task, the potential roadblocks, and important follow-up questions. With advanced analytics, business people can quickly ask questions based on these situations as they occur. Meanwhile, the process moves forward.
  • Measuring impact. Fast data analytics also enable business people to keep up-to-date tabs on their efforts. Instead of waiting until Q2 to find out how a brand performed in Q1, advanced analytics enables users to get answers derived from the most recent refresh of the data source, meaning that they can measure results and act accordingly.

In this sense, business people can spend their time actually creating, instead of waiting on reports or circularly talking through strategies without data to illuminate a clear path of action.

Similarly, data analysts are freed from the slog of routine reports and can instead focus on working with advanced analytics tools that enable them to use their skills directly. Data analysts and scientists can build custom machine learning algorithms and statistical models to run analyses with open source platforms, for example.

In both cases, advanced analytics facilitate a more proactive and action-oriented workflow, which we’ll discuss in the next section.

3. Advanced analytics facilitates proactive decision-making.

Proactive decision-making enables companies to act more innovatively and secure wins without the time and labor costs of a reactive approach.

After all, knowing how a campaign is performing as it is performing enables business people to turn the tides as necessary. And with targeted insights, users will immediately know what they need to focus on to accomplish their goals.

Further, the risks of testing new products and trying new markets increase exponentially when a team can’t react to the results of the campaign in critical time frames. In this sense, teams may only know the extent to which a campaign succeeded after the campaign has been completed.

The self-service component of analytics is important for generating speedy insights. As more analytics platforms implement AI, technology like natural language processing and generation will become more mainstream.

Advanced analytics that’s truly on the edge of innovation involves even more revolutionary tech that reframes the way business people access their data.

With AI, advanced analytics can actively track data fluctuations and determine which changes a user should know. These changes can be targeted to specific users, roles, and functions.

In other words, AI can proactively generate insights that are relevant to a person, without that person having to ask for the insight.

In this sense, advanced analytics tells you what data deserves your attention. This value cannot be understated; the process of determining what to look at is the beginning of all the data discovery that follows.

Asking the wrong questions can obscure valuable insights that should take precedence. AI can take guessing out of the equation and highlight the data most relevant to performance and trends, proactively.

By staying on top of these changes, businesses can act immediately to capitalize on the biggest opportunities for growth and swerve to avoid pitfalls.

Want to see advanced analytics in action? Check out AnswerRocket’s advanced analytics solution.

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