What is data analytics?
Data analytics is the process of discovering patterns in your company’s data. It can be used to predict future events, which can then be exploited to maximize profits. Data analytics relies on advanced mathematics to identify trends that contribute to business intelligence.
Implicit in the definition of data analytics today is vast amounts of data, far too enormous for humans to process without the use of computers. It is a complex field of study, and its uses are so numerous that we can only touch the surface.
The potential benefits of data science can be seen in every business and industry. Data analytics for predictive analysis and event forecasting is used today for everything from early disease alerts to stock portfolio value and program trading. Insurance actuaries use linear regression to evaluate a driver’s probability of having an accident, then calculate the cost of an insurance premium based on the risk assessment.
Data scientists mine data warehouses to answer specific questions, but the hope that data analytics will reveal an unexpected pattern or trend in the data is a parallel treasure hunt accompanying every foray. Indeed, important business outcomes may pivot on such revelations. An accurate bankruptcy prediction, for example, may spare investors significant losses, which justifies the cost of exploratory data analytics.
Data analytics is the collection and analysis of data with the objective of informing business intelligence (BI). The current emphasis in the field of BI is on AI and machine-learning-enhanced analytics.
Here are a few use cases:
- Manufacturing: Factory production sensor data reveals, through data analytics, patterns of factory machine failure leading to predictive maintenance and stocking replacement parts to keep equipment running and eliminate downtime
- Healthcare: Predictive health crisis early alerts based on automated analysis of blood pressure, weight, MRIs, and computer vision analysis of radiographs
- Retail: Customer analytics to personalize transactions and promotions
- Finance: Risk assessment in loan applications and bankruptcy forecasting
- Banking: Fraud detection and other security threats. Predictive analytics for investments.
- Telecom: Call log data analytics forecasts equipment upgrade requirements to keep networks above demand and improve customer experience
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What are the different types of data analytics?
Let’s review the branches of data analytics.
- Descriptive analytics: The process of asking your data the question, “What happened?”
- Diagnostic analytics: The process of asking your data the question, “Why did this happen?”
- Predictive analytics: Using past collected data to answer the question, “What is likely to happen next?”
- Prescriptive analytics: The process of using your data to determine the best path forward
What is the role of data analytics?
The most fundamental role of analytics is to complement and enhance human intelligence. We want machines to provide information, predict the best course of action, and warn us about impending problems — in everything we do.
The roles that data analytics tools fulfill in business include dashboards with predictive charts, visualisations of customer behavior data, and report and presentation creation. This gives companies an understanding of the business model at hand so they can fine-tune their business logic and engender decision-making leading to goal achievement.
Self-service analytics gives access to AI-based analytics to a wider audience. Sisense, for example, offers sophisticated data analytics tools that are accessible for nontech users. It’s easy enough that anyone, even those without a geek degree, can connect their data sources and create a dashboard.
Four ways to use data analytics
There are many proven methods in the data analytics toolbox. But the real excitement comes from knowing that the opportunities are boundless, limited only by your creativity! An innovative mind at Skullcandy, a producer of headphones and audio equipment, imagined that text analytics could be applied to customer warranty information to drive product development.
The endeavor was an immediate success, as analytics revealed that customers were using their products during workouts, and the perspiration was corroding interior components. Skullcandy translated this intelligence into a product improvement success with a little waterproofing. Other case studies also show marked increases in:
- Improved decision-making
- More effective marketing
- Better customer service
- More efficient operations
One of the tried-and-true uses of data analytics is generating intelligence about how to reach customers with the right product at the right instant. Here’s a case study that applies the general principles of data analytics to a situation with real-world complexity.
The sales manager at AllOver Media, an innovative media company, must guide her account reps to success — which is defined as empowering customers to reach target markets — with incisive intelligence from accurate data analytics. Among the challenges she faces are the complexity of data sources coming from multiple platforms in various formats. Data on campaign effectiveness, media type, agency characteristics, and timelines continuously pours into several data warehouses from both clients and their customers.
How can these sources be integrated? How to tie the data together and surface the intelligence? Fortunately, she finds one of the core components of every great data analytics platform in Sisense, a highly flexible tool that connects easily to all data sources and formats. With Sisense plugged into her live streaming data, she builds sales performance dashboards and visualizations to share intel with reps, executives, and middle management teams, improving their decision-making capabilities, collaboration, and efficiency.
Collaboration is central to self-service data analytics success stories. Employees in every department can now use the tools and call the shots. Implicit nowadays in data analytics is the reality of self-service BI access for nontechnical users. Without waiting for IT staff intervention, all users can now build sales performance dashboards that plug into multiple data sources, compare actual versus target data for sales in order to scale up user access, create intelligent reports, and add the ability to share this intel with their clients downstream.
What insights can you gain from it?
Now that we can easily use virtual and augmented reality to provide visualizations, the data collection phase of analytics can be developed through simulations. A cosmetics company, for example, created a virtual makeup simulator app to enable customers to “try on” various colors and styles.
While customers tried on virtual cosmetics, the analytics algorithms beneath the surface recorded their preferences and engaged real-time ML models to make additional suggestions during the shopping experience. The AI bot monitored customer responses to its suggestions.
Then, the algorithm updated and refined its training model, leading to better knowledge of customer preferences and improving customer experience. The experience empowered customers to shop at home, save time, and even keep the recommended social distancing rules.
What are the top data analytics technologies today?
Predictive analytics tools, and especially those that can be operated by non-technical and non-IT users, are gaining popularity among BI solutions. According to Predictive Analytics Today, Sisense is the best overall data analytics tool for the widest audience of users, providing BI users with access to all the core analytics components, including:
- Machine learning: Machine learning means that it is capable of self-learning and takes in various types of data and analyses on its own. ElastiCube is a modern example that can integrate with all data sources and formats. AI and ML essentially provide outcomes that were not explicitly scripted, and algorithms can thus draw the inferences independently of human guidance.
- Data mining: Data mining eliminates the time-consuming work of searching through vast volumes of data to find patterns and discover associations between data points. By sifting through huge databases to determine what matters, companies also use this data to perform analysis and make decisions.
- Data management: Data management requires all data to be organized during the review process and therefore guarantees a higher quality of data can be used if required. A data management platform is the right place to store all the data required for research and future decision-making.
What are the challenges of implementing data analytics?
We have hailed the wonders that can be achieved with data analytics. Is it really that simple, or do traps and pitfalls await the typical business user? What are some of the difficulties typically encountered when launching data analytics?
With massive data comes massive problems. IoT sensors monitoring factory production equipment can generate terabytes of data in a single day. That is truly big data, and it presents several use case issues right off the bat. It is not economical or accurate to process timely data using a cloud app that isn’t optimized for this use case. In fact, this particular challenge recently sparked the emergence of a new subfield called “edge computing.”
Edge computing is a new area of data analytics that applies analytics methods to data at or near the source. Rather than transporting data via an enterprise service bus or other paradigm, the edge compute strategy identifies data that is time-dependent, from which predictive outcomes must be extracted essentially on the spot. The processing is done immediately, and only the results are shipped to cloud apps for visualizations and other applications.
In other words, the amount of data being collected, although the very bread and butter of data analytics, can be an unwieldy commodity requiring an engineering paradigm all its own. This subject of data volume is closely related to another challenge: integration.
Every enterprise software generates and stores data in its own unique proprietary formats. The most popular enterprise resource planning apps, customer relationship management apps, for example, contain APIs to facilitate integration with other apps. However, there is no universal connector. Integration middleware is a software industry in its own right.
What is the future of data analytics?
Like asking the fortuneteller to predict her own future, can data analytics tell us anything about where we are headed? One thing is for certain, the trend toward easy access is gathering speed. Natural language processing advances ensure that communications between users and AI-driven analytics platforms like Sisense will reach increasingly higher levels. In other words, users will be able to instruct AI translators that will in turn auto-generate code to build increasingly complex intuitions into our cross-referencing data paradigms. It’s an exciting future, indeed.
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