As a leader at a small but growing business, you’re well aware of the importance of using analytics to accelerate growth and keep pace with your competition. But with increased access to emerging technologies, you need to evolve from basic analytics to advanced analytics applications to make the most of your business intelligence efforts.
In fact, Gartner predicts that by 2025, advanced analytics applications and AI models will replace 60% of existing models based on traditional data analytics. 
In other words, advanced analytics is quickly becoming the new baseline for modern business intelligence teams, and organizations that aren’t taking advantage of these advanced capabilities are as far behind as a team that wasn’t using analytics at all 20 years ago.
In this article, we’ll look at some tools and tactics that growing businesses can use to boost their advanced analytics capabilities, backed by Gartner research. 
Advanced analytics is a technology-boosted form of business intelligence that uses emerging technology including data mining, machine learning, pattern recognition and AI forecasting to automate data analysis, uncover insights, generate predictions and recommendations, and more.
Before we dive deeper into advanced analytics, it’s important to understand what makes advanced analytics so advanced compared to traditional analytics and other types of analytics.
The difference between traditional analytics and advanced analytics really comes down to technology. While traditional analytics can (and have been) be performed for decades using available technology—calculators, spreadsheets, and basic databases—advanced analytics rely on emerging tech like AI, machine learning, and big data to derive deeper, previously unavailable data insights. The use of this specific technology to aid with tasks such as data preparation and insight generation is also referred to as augmented analytics.
The difference between analytics and advanced analytics is almost like the difference between a propeller-powered aircraft and a supersonic, jet-powered aircraft. Automation is a big part of advanced analytics, but not all advanced analytics are automated. For example, a human data scientist might use advanced analytics techniques to process a massive set of data and generate insights, and then manually create data visualizations based on those insights.
Introduction to Advanced Analytics Tools and Techniques 
To better visualize how advanced analytics can benefit your organization and accelerate growth, let’s look at some real-world examples of advanced analytics applications.
A real estate company can use advanced analytics to analyze thousands of real estate listings and determine accurate valuations in a highly volatile market.
A finance company can use natural language processing to interpret and summarize financial performance information for thousands of clients when using human writers would be cost prohibitive.
Companies across industries can use a machine learning algorithm to process internal and external data in order to produce more accurate budget and cash flow forecasts.
Businesses can use advanced analytics in a similar way to generate more accurate customer churn predictions, employee attrition rates, and fraud.
These are just a few examples of how companies have already been using advanced analytics. But with enough data and the right technology in place, advanced analytics can be used to make predictions about future market conditions, business revenue, and even staffing levels.
Advanced analytics tools are powerful, but you still need the right employees in place to fully take advantage of these tools. Here are a few key roles that you can employ to support your advanced analytics efforts.
Data steward/data custodian. Data stewards, also referred to as data custodians, are any individuals in a business or organization who are responsible for monitoring a data source and enforcing data governance policy , either in an officially designated or implied role.
Data manager. A data manager is responsible for maintaining and upholding an organization’s data governance policy. The data manager is also responsible for overseeing and guiding the data stewards.
Data owner. A data owner is any individual in an organization who is directly responsible for the protection and quality of a set of data or multiple sets of data. Data owners typically work with data managers to discuss access to shared data and the state of the data governance policy. At a smaller organization, these data management responsibilities might be condensed under one role.
Data analyst. The data analyst studies and then derives insights from data to prepare reports and present findings to leadership using data visualization. The data analyst will be familiar with augmented analytics tools such as AI and machine learning, and the latest business intelligence and analytics software.
Data scientist. The data scientist shares similar responsibilities with the data analyst, but with the added responsibility of building computer models, using advanced analytics tools to identify patterns, and making predictions based on those patterns. In bigger organizations, this role might be divided into data engineers, who build algorithms and oversee database structure and pipelines, and scientists, who work with the data itself.
Data and analytics leader. The data and analytics leader is responsible for the oversight and management of all of an organization’s data and analytics teams. A data analytics leader should also have a large role in managing and connecting data silos.
Chief data officer. In larger organizations, the chief data officer is responsible for the governance of all data across the organization’s data warehousing system. The chief data officer might manage several data and analytics leaders.
Gartner recommends a three-pronged approach to boosting advanced analytics adoption.
“Culture hacking” is a term for making small, organizational changes in an effort to make progress toward a much larger, transformational goal (like adopting advanced analytics throughout your organization).
Here are a few examples of these culture hacks to try:
Hold “fear of change” forums to demystify advanced analytics and help less data literate teams and individuals become more comfortable with advanced analytics concepts.
Use advanced analytics-derived data findings in company-wide presentations, and explain where those findings came from.
Deploy data and analytics ambassadors to give presentations and hold workshops with teams from across your organization.
Advanced analytics is a complex, constantly evolving field, and a lack of data literacy can be a roadblock to company-wide adoption.
Here are a few examples of strategies for improving company-wide data literacy:
Create an in-house dictionary of commonly used advanced analytics terms that can be featured on your company intranet.
Hold regular data workshops to highlight advanced analytics use cases and answer questions about these initiatives.
Reimburse employees for enrolling in and completing data training through online universities and learning management system course libraries.
Hiring new advanced analytics talent every time you have a new project can be cost prohibitive, and training staff with no advanced analytics experience can be time consuming. An alternative approach is to network existing talent across teams to create a multiplier effect. This approach offers obvious benefits for teams that are learning new skills, but also for the employees who are sharing their skills and experience.
Here are a few examples of those benefits to highlight when proposing this approach:
Expanded network and impact across the organization.
Increased visibility with other teams and exposure to senior leadership.
Development of new skills from working on different types of projects.
Want to learn more about interactive dashboards? Check out our guide here.
In this article, we looked at what it might take to start using advanced analytics to grow your business.
To summarize, here are some things that you can start doing right now to pave the way for effective advanced analytics adoption:
Foster a company-wide data-driven culture through small changes like deploying data ambassadors.
Standardize data literacy training and hold regular data workshops.
Encourage employees with advanced analytics experience and capabilities to network throughout your organization and bring their skills to cross-team projects.
Taking these steps will give your business a huge head start toward successful advanced analytics adoption, but it’s just one piece of the puzzle. It’s also crucial to get the right technology in place, and we have you covered on that front.
Ready to explore business intelligence software to boost your advanced analytics adoption? Check out our Category Leaders in Business Intelligence Software for 2022 here. This list features 15 top options in BI software based on thousands of verified user reviews.
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