Augmented Analytics – Empowering Non-Technical Teams with AI Insights
In the current data-driven economy, organisations find themselves sitting on top of a gold mine of information; however, the problem seems to be that of interpreting the information into meaningful insights. At one point, data scientists, as well as analysts, were solely the ones to transform raw data into actionable intelligence. However, there is a revolutionary method of democratising the ability: Augmented Analytics.
Augmented analytics utilizes artificial intelligence (AI), machine learning (ML) and natural language processing (NLP) in automating data analysis to provide deep insights to non-technical users, often in self-service, user-friendly forms such as HR executives, store managers or marketing leads.
This change is already speeding up decision-making and propagating a more nimble and data-literate workforce.
What is augmented analytics?
Augmented analytics is the automation of data preparation, the discovery of insights and sharing the same through the implementation of AI and ML. As compared to traditional business intelligence tools that needed technical skills to operate, augmented analytics allows users to explore data on their own, in many cases with the help of a drag-and-drop interface or conversational queries.
Insightsoftware defines augmented analytics systems as powers that improve the human factor of data analysis through the provision of automated insights and suggestions that are based on contexts, patterns, and relationships in data. Such tools do not substitute the decision-maker themselves; rather, they complement by bringing to the fore insights that could not be noticed otherwise.
Six Capabilities of Augmented Analytics
- Self-Service Automated Data Preparation: Transforms, cleanses and integrates data across sources- no need to use SQL or code.
- Natural Language Processing (NLP): Offers the ability to pose queries such as, What were the best-selling items last month in Mumbai and be able to come up with a response in an instant and a visual manner.
- Insight Discovery from AI: The correlation between surfaces, outliers and predictive patterns, which are not necessarily searchable by the users.
- Conversational Analytics: Gives information using chat-type interfaces, making it more approachable to everyone.
- Predictive Analytics and Prescriptive Analytics: Projects and predicts (e.g. predicts future sales) and prescribes the best course of action (e.g. when to make a stock part).
- Embedded Dashboards: Interactive visual dashboards may be integrated into the application that teams are already using, such as CRM, ERP, or POS.
Case study: A retail chain increased sales by 10% through augmented analytics.
Background
A national retail chain of stores, which has more than 150 outlets in urban and semi-urban areas, faced a problem in inventory management. Managers of the stores were forced to make use of spreadsheets and regular accounts provided by the central analytics team. These reports were usually not up to date, and there was:
- Storage of goods that are low in demand
- Stockout during rush periods
- Weak sensitivity to the local tastes
- Poor inventory management had a direct effect on the customers and sales.
- Application of Augmented Analytics
To resolve this situation, the chain implemented an augmented analytics platform in each of their stores and compatible with their POS, CRM and ERP solutions.
1. Easy-to-use Dashboards for Store Managers
As opposed to the head office reports, managers now received near real-time dashboards which indicated:
- Sales trended in the daily and weekly sales
- Turnover ratios of inventories
- Demand for the product by hour and by day
- Automated low inventory or fast-moving stock messages
The dashboards deploy AI models behind the scenes to recommend restocking levels based on historical demand, weather conditions, activities around the event and the foot flow.
2. Predictive Inventory Management
The system was able to predict future demand using a more than 90% accuracy rate by analysing the past sales and the current trends. A good example would be when CRE suggests that one given store in Pune needs to buy more raincoats 2 weeks in advance, or that it would help another place by decreasing their formalwear stocks after noticing a continuous fall in sales.
3. Natural Language Queries
Questions that managers may type will be:
- What are the products which are likely to become out of stock within this week?”
- What is chasing away customers during the weekdays?”
There would be no technical assistance, and the system would be able to provide visual answers in the form of graphs, tables and written explanations.
4. Mobile Accessibility
The management system was wasmobile-optimisedd, which provided managers with an opportunity to access data on the shop floor, during a meeting, or even at home.
Measured Impact
10 per cent increase in the store’s level of sales in the first half year.
A 30 per cent decrease in out-of-stocks due to active refill.
Decision making took a 50 per cent shorter time, hence managers did not wait to be instructed by the head office.
Level of adoption: 80 per cent adoption across stores, demonstrating the fact that it is easy to use even by non-technical people.
Advantages to Non-Technical Teams
1. More, More Rapid Decisions
Non-technical users are able to avoid bottlenecks of overworked data teams and obtain real-time visibility of the operations. This flexibility will be of the essence in dynamic environments such as retail, manufacturing and marketing.
2. Higher Empowerment and Accountability
Being able to access the data with their fingertips, the employees feel more responsible and better prepared to act. As an example, a marketing leader will be able to see at the moment of launching a campaign, such as whether it brought engagement or a conversion.
3. Improved Collaboration
The information is distributed in small, easy-to-digest formats that allow combined efforts among departments and enable the finance department, the sales department, human resources and operations to work much better when sharing a common data truth.
4. Cost Reduction
Automation of insight generation moves data science out of reliance on costly data science consultants or full-time analysts to produce every analytical task.
Issues to Adoption
Although there are advantages, there are no challenges to implementing augmented analytics.
- Garbage in, garbage out: Data Quality Problems. Integrated, labelled and clean data is crucial.
- Change Management: There is a possibility that the unfamiliar tools can be resisted by the non-technical employees. It also needs appropriate training and change management.
- Excessive reliance on Automation: The application of AI understanding is not supposed to substitute human judgment.
- Security and Governance: Self-service analytics must be modelled to support the data governance policies and avoid misuse and unauthorised access.
Best Practices of Augmented Analytics
- Begin with Particular Use Circumstances: Optimising inventory, churn prediction, or sales forecasting will be good points to start with a reachable ROI.
- Engage the End-Users Early: Tool selection and the design of dashboards are supposed to include store managers or the employees on its frontline to make it usable.
- Invest in Data Readiness: Ensure that data pipelines are clean and accessible. Reconcile POS, CRM, ERP, and third-party data in the provision of contextual insights.
- Learn Data Literacy: Introduce short seminars on the topic of understanding the trends and recognising the right questions, and using the AI information in the decision-making process.
- Monitor and better: Monitor tracking, performance, and business results constantly. Incorporate new models and dashboards on changing requirements.
Future of Work and Augmented Analytics
- Augmented analytics does not just stop at retail. The next several years:
- Treatment outcomes, as well as patient risks, will be identified by healthcare teams using it.
- The HR departments will forecast attrition and plan the workforce.
- The finance teams will do what-if simulations in investment planning.
- The manufacturing teams will predict machine breakdown and minimise the production cycles.
The distinction between data experts and business users will be destroyed as the AI becomes more conversational, and the domain experts will be replaced by a hybrid workforce with domain knowledge and analytical capacity.
Conclusion
Augmented analytics is changing the face of the way organisations make decisions by bringing AI-driven insights into the hands of non-technical users. The case study of the retail chain stores demonstrates that once the employees get timely, relevant and actionable information, the level of smartness of their decisions will improve, and the business benefits will become evident.
By applying the proper approach, organisations, regardless of size, may minimise reliance on central data teams, improve their responsiveness, and achieve the full potential of their talent. Not only is the future data-driven, it is also AI, democratised, and agile.
The moment has come to provide your team with the solutions that would allow everyone to become an analyst.
