We are living in an era of big data. All of this information is growing and comes from major sources like Google, Amazon, Facebook, Twitter, Netflix, and more. These sites build user profiles and are always adding more information, content, and products.
To handle all this data, we need new Business Intelligence tools that can speed up the analytics process because traditional tools just aren’t cutting it. Enter: business intelligence and augmented analytics.
Before we discuss what augmented analytics is, we first have to discuss the importance of data-driven marketing, as well as the business intelligence tools that make augmented analytics possible.
The Importance of Data and Analytics
Different types of organizations, like life sciences, banking, health care, government, retail, and manufacturing, have large volumes of data. Data analytics establishes the foundations for these organizations. Businesses rely on quick and effective decisions to stay competitive. Analytics helps them make those decisions.
Big data analysis works in two ways:
- By focusing on gaining insights into data
- And by developing models to improve decision making
Findings and results from data analyses provide input into marketing and business development decisions. The developed insights and models add value to organizations in three ways:
- Decision support for marketing
- Improved actions and campaigns
- Information-based products and solutions
What is Business Intelligence?
Business Intelligence is an intelligent approach to run a business that focuses on developing new insights and models based on data and statistical methods. Business intelligence is based on statistical analysis, predictive modeling, and human management.
Intelligence in business can be achieved using business intelligence (BI) tools. Business intelligence tools can help companies make better decisions by showing past and present data visualization. With BI tools, you also need a team of analysts who can manage the data and leverage it for insights.
BI tools help you discover insights such as:
- Ways to increase revenue
- Valuable market insights
- Competitor data
- How to increase customer satisfaction
- Performance tracking
- Operational inefficiencies
- Potential business issues
- Accurate decision-making
Examples of Business Intelligence Tools
Once data is in a data warehouse, business intelligence tools work to analyze it. Let’s discuss some widely used BI tools:
SAP Business Intelligence
With SAP Business Intelligence, we get real-time business intelligence. It is a self-service business intelligence tool that provides modeling tools for geo analysis and multi-level hierarchies. It supports functioning as a cloud platform, as well.
SAS is a self-service tool that allows us to leverage data and metrics to make informed business decisions. SAS provides high-level data integration, advanced predictive analytics, and reporting within a great business intelligence platform.
Tableau is a user-friendly tool that can help anyone analyze and understand data. Tableau is compatible with many data sources such as MS Excel, Oracle, MS SQL, Google Analytics, and more. Tableau can connect to almost any database and provide fast visualizations using drag-and-drop features.
Sisense is a user-friendly tool that can be used by any technical and non-technical employee from the organization. It brings data together from a wide variety of sources and provides analysis and visualizations of large and complex datasets very quickly.
QlikSense is a new generation analytics platform and a product of Qlik. Qlik Sense explores data and creates a relationship between every aspect of information. It reveals insights you can’t see with query-based BI tools and provides a smarter user experience and faster analysis.
MicroStrategy is a BI tool that offers quick, powerful dashboarding and data analytics to monitor trends and recognize new opportunities. It can collect data from various sources like spreadsheets, cloud-based, or enterprise data software.
Microsoft Power BI
Microsoft Power BI is a web-based business analytics suite that excels in data visualization. It allows users to identify trends in real-time and because it’s web-based, it can be accessed from anywhere. Users can integrate their apps with Microsoft Power BI to deliver real-time reports and dashboards.
The Challenge of Integrating Business Intelligence
There’s no denying the value of business intelligence and the beautiful data visualizations they can create. But, businesses still need people who can analyze that data and form strategic conclusions. Data scientists help generate these insights.
Data is useless for businesses if not interpreted correctly. Let’s look at an example:
For the marketing team, analyzing customer behavior on the website is valuable. This data could include past purchases, purchasing patterns, and popular products. Data scientists can then analyze this past data, create a model to interpret the behavior, and generate insights accordingly. With the correct insights, the marketing team can then use those to drive more sales and attract new customers.
The problem, though, is that data scientists are expensive. According to Glassdoor, data scientists have a base median salary of $110,000. For a small or medium-sized company, this is a major expense.
The good news is that many of the tasks that data scientists focus on can be automated with the right tools, therefore reducing cost. This is where augmented analytics come in.
What is Augmented Analytics?
In simple terms, augmented analytics is business intelligence and artificial intelligence combined. Augmented analytics implements automation using machine learning to supplement human intelligence across the entire analytics lifecycle.
Augmented analytics uses machine learning and AI to supplement data science tasks like data preparation and analysis. It can even make insights and data visualizations. Augmented analytics can help save a business money and is more efficient than previous options.
Benefits of Augmented Analytics
There are countless benefits of using augmented analytics for your business. Some of them include:
- Having the power to use data for insights
- Giving everyone across the organization access to data
- Provisioning business intelligence quicker and with fewer resources
- Quicker data cleaning and preparation
- Automated insights, analysis, and discoveries
- Timely and accurate business decisions
- Time and money savings
- The ability to achieve mature modeling goals
- Reduced human dependency
- Reduced errors as human predictions are prone to higher errors
The Relationship Between BI Tools and Augmented Analytics
The goal of most business intelligence tools is to create analytics visualizations to then make insightful decisions. Because the BI tools don’t do the analysis, companies need data scientists or analytics teams to generate these insights.
On the other hand, augmented analytics can conduct analyses and generate insights automatically with little to no human supervision. These insights can be used by marketers, business owners, and any other decision-makers in an organization. Some BI tools are beginning to integrate with augmented analytics.
The Future of Augmented Analytics
We are already seeing some progress in the field of augmented analytics and it’s only getting better. The future of augmented analytics is divided into three steps: data cleaning and preparing, pattern detection, and actionable findings.
Data Cleaning and Preparing
Data cleaning and preparation are very tedious tasks and can be completely eliminated using augmented analytics and BI tools. Some of the BI tools, like QlikSense, IBM Watson, and Tableau, are already doing it.
The next task that augmented analytics can help with is pattern detection in the data. We still need a data scientist’s help to convert those patterns into concrete business insights, but augmented analytics helps save a lot of time. Many companies are working on this stage and will likely reach this stage in the coming years.
At this stage, augmented analytics will suggest actions based on the patterns detected. These actions will be predicted using past data and actions taken by data scientists.
The system will also be able to track the implementation of these actions and provide additional insights so that the company can do better next time and maximize its operational effectiveness. This makes augmented analytics engines a more accurate and efficient substitute for business analysts.
This stage requires more time to implement compared with the previous stages.
In the past few years, data science, analytics, and artificial intelligence has come a long way. Now, thanks to collaborations with business intelligence tools, augmented analytics is beginning to progress. There is a lot in store for the future and businesses of all sizes will be able to take advantage of this technology.