In the current context, companies are widely aware that their greatest wealth lies within them: the company’s data assets assume a competitive strategic value that represents a missed opportunity if not treated effectively and efficiently.
If Business Intelligence has helped companies make better decisions by transforming data into information and knowledge, Advanced Analytics has more ambitious goals: increase the value of the same data by using them to predict future events and make the best choices for your business.
In this sense, Advanced Analytics represents the natural evolution of Business Intelligence.
What is Advanced Analytics
Advanced Analytics is an advanced analysis system based on complex machine learning (ML) and autonomous or semi-autonomous data analysis to predict future trends and business results (Gartner Glossary).
Advanced Analytics is therefore not a technique, but a set of techniques used to go beyond traditional analysis: for example, through real-time analysis, predictive modeling and statistical methods applied to business intelligence tools (BI and BA), Advanced Analytics allows you to predict scenarios, make recommendations, and make future recommendations before they happen. This allows companies that use it to have a huge strategic advantage to dominate and thrive in highly competitive and changing markets.
Why Advanced Analysis is useful
With Advanced Analytics solutions, companies can:
- Increase ROI on your investments: optimizing operations, innovating processes and improving performance.
- Predict the future: Advanced Analytics allows companies to analyze historical data and business information, overlaying them with real-time data to make reliable predictions.
- Reduce risk and loss: decision-making processes rely on reliable data even in particularly changing scenarios.
- Innovate the business to anticipate trends (even influence): gaining a competitive advantage over the main competitors in the sector.
- Optimize resources and processes: reducing costs and moving resources to more productive areas.
Application areas of Advanced Analytics
So the evolution of data analysis passes from Advanced Analytics systems and tools that go beyond the study of historical data, looking for possible correlations between predictive and even prescriptive approach:
- Predictive Analysis: providing future predictions on known or unknown events, based on historical and current data, through predictive modeling, machine learning and data mining techniques.
- Prescriptive Analysis: starting from the descriptive and predictive analysis, specific innovative platforms suggest the best actions to take to achieve certain goals, also showing the effects of each decision.
In this way companies take advantage of a huge decision support, basing their choices on current and future concrete data, knowing in advance the effects of their actions and, sometimes, choosing to automate certain activities.
Advanced Analytics techniques and features
Advanced Analysis software and solutions include techniques of:
- statistical modelling
- machine learning
- data/text mining
- data visualization
- network and cluster analysis
- sentiment analysis
- simulation
- analysis and processing of complex events
- neural networks
- cognitive computing
- and much more
These techniques are used to perform advanced analysis with two main objectives:
- Augmented Analysis: expanding insights and predictions to support decision making;
- Automate decision making by supporting and suggesting human intervention
How to use Advanced Analysis in the business environment
Every business area can benefit from the benefits of advanced analytics:
- Finance – Risk Analysis: react in a timely manner to changes in market conditions, increase performance and productivity, predict economic and financial risks.
- Predictive Maintenance: keeping equipment, computers, industrial machinery in optimum working order by applying AI to anticipate failures, avoiding failures and, above all, machine downtime and therefore production interruption in the smart factory.
- Forecasting: alongside the typical forecasting process based on the intuition of business figures, the use of AI algorithms that allow simulation of events and scenarios of various nature producing accurate forecasts and allow you to react promptly to any change.
- Marketing & Digital Strategy: understand and predict the needs of its customers, identify the most appropriate strategies and develop personalized marketing to improve the customer experience.
- Sales & Commercial: identify product, price and market logics influenced by future events and trends, develop commercial strategies for specific markets and geographical areas.
- SupplyChain Optimization: optimization of production processes, reduction of distribution costs, predictive maintenance to avoid machine failures and delays.
- Help Desk: offer support to internal structures through, for example, the use of chatbots able to respond in a complex and proactive way, allowing the human to devote himself to strategic activities .
- Human Resources: improving employee satisfaction and retention in the company.
These are just some of the main opportunities offered by solutions and software that integrate advanced analysis techniques.
Advance Analysis Tools vs Solutions
There are numerous advanced analysis platforms, each with advantages and disadvantages depending on the use case.
- Platform: Dataiku, SAS, Microsoft Azure ML and IBM are tools that allow advanced analysis, ensure predefined models, visual and graphic approach (not only through code) and often enable democratization (can be used not only by engineers and technicians). Although less economical than open source solutions, the proprietary tools of Advanced Analysis have the great advantage of ensuring continuous automation and support (as well as being engaged in continuous investments to improve the product).
- Open source tools: generally less expensive, they are tools supported by a community of users actively engaged in improving functionality (e.g. R, Python). Although support and documentation are entrusted to the community, there is no real guarantee and support; Moreover, the lack of tools and boundary objects that allow to administer, automate and visualize better algorithms and processes, is an aspect to take into account.
- Self-service analysis tools: platforms that make advanced analytics easy and intuitive for business users (e.g. Tableau, Qlik). Although they are more immediate to implement and use, the features remain more limited than the more specific technologies because they are essentially born as tools for Business Intelligence and Data Visualization, rather than for Advanced Analysis. The advantage is that they can be integrated into more complex solutions.
How to Implement a Company Advanced Analysis Model
Implementing Advanced Analytics projects in the company is today a definable condition of survival in the market and not only more distinction but remains a complex process that requires numerous technological skills-functional and business processes typical of each sector.
Although some self-service analysis tools are faster to implement, they may not always be the right answer for the specific needs of the company and also for the skills you have inside. In addition, before starting a path to the AA you will need to understand the reality of the company and whether the AI or ML can support the process, evaluate if you have a large amount of data that you can expect the answers you want and finally if the chosen technology fits well within your ecosystem.
Today, more and more companies are structuring themselves by setting up teams of data scientists capable of developing complex projects with open source tools. At the same time, there are today end-to-end platforms on the market that aim to bring AI within less structured contexts but that see Advanced Analytics as innovative support in their business choices.
Blue BI’s Advanced Analytics team encompasses a mix of skills both at the AA discipline level and at the platform knowledge level to support and improve the path that companies want to take with AA.
The AA Team has expertise on:
Some of the BBI experiences:
- Predictive maintenance in IoT environment
- Clustering of points of sale
- Development of sales forecasting models
- Automation of processes for comparison between customer requests
- Adoption of preventive strategies to avoid customer diversion
We realize Business Intelligence & Advanced Analytics solutions to transform simple data into information of freat strategic value.
