Every growing company taps into analytics to improve business processes by developing a better understanding of their market, audience, and internal performance and productivity. But analytics is a dynamic field. With growing computing ability, artificial intelligence, machine learning, and cloud computing technologies, business analytics has become more advanced.
Some companies have progressed more than others, and some have still not found the answer to the question, “how do we become a more data-driven decision-making organization?”
How is Advanced Analytics Different From Analytics?
Advanced analytics is distinct from traditional descriptive analytics or business intelligence. It uses predictive modeling, statistical methods, machine learning, and process automation techniques to analyze data or business information. It leverages big data and data science to rely on mature analysis methods to gauge future trends and predict the likelihood of potential events.
It empowers companies to forecast future trends or outcomes for a deeper understanding of the business. Advanced analytics further offers a wider set of capabilities to deal with challenges that are not possible to achieve with traditional BI software, hence enabling stronger strategic decision making for the future.
What Value Advanced Analytics Adds To Business?
Advanced analytics provides a way for businesses to get more value from operations and data assets. It may include the use of predictive analytics or recommendation engines. It helps many businesses to extract value from real-time data generated by business operations or data stored in a data warehouse. Businesses that use advanced analytics tools can build on the discovery of knowledge to inform the likelihood of future events and provide greater confidence for more repeatable business results.
Data mining, data visualization, machine learning, pattern matching, network, and cluster analysis, sentiment analysis, forecasting, complex event processing, and more are some of the organizations’ top advanced analytics techniques. All or some of these techniques can be employed to trace and model company data to improve a business’s day-to-day functions based on actionable insights.
Top Advanced Analytics Techniques or Capabilities
The following capabilities have gained traction from the past many years under the umbrella of advanced analytics:
1. Predictive Analytics:
Data analytics is used to make predictions about future business outcomes. The process includes using historical data and statistical modeling or regression models, and machine learning methods.
Aggregation over various calculation tools like visualizations, expressions, etc., can be used in advanced analytics.
3. Machine Learning:
Machine learning algorithms learn to build detailed models from data to identify complex patterns and make highly accurate predictions. They are suitable for use cases such as micro-segmentation, personalization, root cause analysis of complex processes, fraud detection, and customer churn.
4. Statistical features:
It is important to have statistical features to perform advanced analysis such as clustering, comparison circles, box plots, and relationships between categorical variables.
It is a process of clustering things together based on similarities between the data in the group to generate information out of it.
Why Does Advanced Analytics Matter Now?
Since high volumes of data are being generated daily, companies are exploring innovative ways to put data into use. Advanced analytics helps companies do just that by enabling them to optimize their operations and innovate to gain a competitive advantage. With improved customer analytics, predictive analytics, and statistical modeling, advanced analytics is helping companies improve decision-making and keep pace with extremely competitive, rapidly changing markets. Where traditional analytics and BI tools don’t hold up, advanced analytics drives companies to future success.
Top Use-Cases OF Advanced Analytics
The advantages of advanced analytics are multiple and varied based on different industry requirements. We have mentioned below some of the top industries using advanced analytics to optimize business processes and improve their capabilities:
1. Supply Chain Adaptation:
Global enterprises require a supply chain that can respond quickly to changes in market conditions, including fluctuations in market demand, customer preferences, and costs. Advanced analytics, especially in manufacturing, can help reduce delivery costs, avoid delays, or machine failures, and streamline processes.
2. Business Operations:
Advanced analytics helps companies be agile and enable operational excellence to respond to changing market conditions faster and optimizes decisions based on anticipated future trends. It allows organizations to improve performance and productivity, increase revenue as well as resolve operational issues faster.
3. Risk analysis:
Every business looks for ways to mitigate future risk, but only those that employ advanced analytics use large data sets, or even data streams, in real-time to identify risk patterns. Advanced analytics techniques are used to detect fraud in payments and orders or monitor reputation among existing customers.
4. Marketing Analytics:
Understanding customers has never been more difficult, and it can be a huge challenge to properly sell and market those customers at the right time. With advanced analytics, companies can create personalized sales and marketing campaigns to reduce spending on inefficient strategies, identify up-sell and cross-sell opportunities, and improve customer experiences.
5. HR Analysis:
Employee satisfaction has a huge impact on business performance, yet most companies fail to take advantage of HR data. Advanced analytics in HR can be used to reduce employee turnover, reduce costs associated with recruiting and hiring, and invest in effective rewards programs to maintain or increase employee satisfaction.
Advanced analytics helps answer tough business questions. If you are not using data to answer some of your toughest business questions, you are probably not using advanced analytics to your full advantage. Consult today with a vendor having experience in data engineering or data science projects and explore how you can put your enterprise data to work!