What is advanced analytics?
Advanced analytics is the technology that uses machine learning algorithms to analyze large volumes of information to discover patterns and predict events in a company’s production processes.
To do so, it uses mathematical knowledge that yields statistics on all areas of the company. In addition, it is characterized by being predictive (what will happen) and prescriptive (what should I do to make it happen), so it could be said that it acts in the present with data from the past to make calculations about the future.
That is why it is so important that the information collected by a company is comprehensive and of high quality (ordered, verified and aligned with business objectives). Only then will this technology be able to make more robust analyses and more accurate guesses.
Moreover, based on this, executives can make agile and informed decisions about which business actions and activities should remain the same, which require some adjustments, and which should be changed altogether.
Also, advanced analytics offers solutions to model the desired results in a business strategy; for example, what promotion to launch to increase customer engagement or how to face market conditions at a specific moment in time.
Advanced analytics turns data into business strategy
What are the benefits of advanced analytics?
Through advanced analytics, companies can make their human, material, technological and financial resources more efficient because:
- They use predictive models to identify the most profitable customers, as well as those who have the greatest potential to invest and those who are most likely to cancel a service or not buy a product again.
- They combine internally generated data and data acquired from external sources to run a detailed analysis and thus have a comprehensive understanding of their customers.
- They optimize their supply chains, thereby reducing the impact of an unexpected constraint, simulating alternatives and diversifying their distribution.
- They set prices in real time to get the highest possible return on each of their customers’ transactions.
- They create complex models on the relationship between their operating costs and their financial results.
Therefore, companies using advanced analytics understand that most of their areas and functions, even those that are more creative such as digital marketing, can be optimized with quantitative techniques.
In short, organizations that apply this technology do not gain advantages from one revolutionary application, but from multiple applications that support their internal areas, which translates into benefits for customers and suppliers.
<< Advanced analytics vs business intelligence, what is the difference? >>
How do you know if a company is ready for advanced analytics?
For a company to successfully implement advanced analytics, it is recommended that it identifies its analytical maturity, for which it needs to evaluate its databases and their life and growth cycles.
An effective way to measure analytical maturity is the DELTA model, explained by Thomas Davenport in his book Competing on Analytics. There, the author mentions that the successful combination of Big Data with advanced analytics creates a Key Competitive Advantage.
Any company can achieve this key competitive advantage by strengthening the following capabilities:
- Data. The data generated, collected and processed are accessible and accurate.
- Enterprise. Technological tools and databases are available for all productive areas.
- Leadership. Managers encourage internal users to use data correctly for analytical processes.
- Target. There are clear and measurable goals, with adequate budgets and well-defined tasks and actions.
- Analysts. The staff includes people trained to run and manage advanced analytics models under a dynamic approach.
These factors provide insight into the analytical maturity of an organization: the more it complies with them, the more capabilities it has to perform successful advanced analytics processes:
- 5 factors – Analytical competitor
- 4 factors – Analytical organization
- 3 factors – Analytical aspirant
- 2 factors – Isolated analysis activities
- 1 factor or none – They do not consider advanced analytics.
What challenges must advanced analytics overcome?
The adoption of advanced analytics in a company implies changes in the culture, processes, behavior and skills of all employees.
This transition requires a passion for the quantitative approach and leadership on the part of the organization’s managers. Examples of CEOs who have pushed advanced analytics in their companies in recent years include Loveman of Harrah’s, Jeff Bezos of Amazon and Rich Fairbank of Capital One.
It is also important to have the assistance of
experts in the field
Before implementing an advanced analytics model, they will evaluate the needs and objectives of the organization.
It is important to clarify that companies must be clear that adopting an analytical model does not imply that all decisions depend on it, since the human factor, the context and other more intuitive aspects provided by experience must always be taken into account.
Therefore, analytically minded CEOs are faced with the challenge of knowing when to follow the numbers and when not to.
Do you apply advanced analytics in your company? Based on the above information, what is your level of analytical maturity? How could an analytical approach help your company’s production processes?
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Originally published in Jorge Pérez Colin Blog
