Blog Post

Power of staying competitive in an ever-changing world

In todays’ fast paced world, data is the foundation of any business activity, with an avalanche of data available for functions such as supply chain, sales, operations, and finance. Managing this volume of data, which in most cases is unstructured, obtained from numerous sources usually with challenging deadlines can be overwhelming & time consuming. The output being that only basic insights can be drawn in a timely manner, which is not necessarily conducive for key decisions to be made to drive a business forward.

For those companies who turn to and engage with data analytical solutions, they can extract meaning and relevance at speed which improves and shapes their decision making, while also significantly increasing their ROI. These businesses have seen results such as lower operating costs, increased revenues, significantly improved working capital and for manufacturing businesses reduced inventory, and improved product mix.

This data only has a value if you can extract the detail that is relevant to you at the time that you need it. Digitization has solved some of these problems but not all of them. Digitization allows for basic search functionality, but it still requires a huge amount of time, and the real risk associated with human error. Data science techniques such as predictive & prescriptive analytics, data mining and machine learning are a few of the ways in which we can extract real value from unstructured data when required.

Missing and partial data loss due to being consolidated, grouped, censored, truncated, or rounded, is another area that causes significant headaches and consumes huge amounts of time that could be otherwise spent on analytics and business partnering. The value of the data that remains, is dependent on if we can identify reasons for the data being missed/lost in the first place.

Using various techniques to identify the reasons for and then solve this problem include, missingness mechanism categorisation, causal inference & semiparametric theory.

Finance is one function that has access to more data than most. FP&A professionals are predominantly focused on forecasting, which requires a need to have knowledge of and be able to engage with the whole business. However, 90% of their time is spent on obtaining data before being able to analyse it.

“Every 1% increase in forecast accuracy, the step change can be dramatic to a business’s bottom line.”.

Due to the impact of Covid-19, and the uncertainty this has brought, many businesses are finding it harder to forecast or budget; they cannot rely on past data to predict future results. The impact flows through the whole business, from sales demand, supply chain, churn, mix, and expenses.

One way to mitigate this, is by the combined use of scenarios and the various analytical techniques which Data Science & AI has to offer.

Businesses that take the decision to actively engage by taking the steps to transform from descriptive analytics through to predictive analytics and then finally prescriptive analytics reap huge benefits.

All these methods have their place, they can co-exist with one another. There are benefits from applying different types of analytics, plus their relevant tools and techniques across a business and its functions dependent on the situation.

Descriptive analytics, as it says on the “tin” are used to describe what has happened in the past. It is useful, as it can be used to study past behaviours, what is happening now and understand how it can influence the future. However, Covid-19 has made this form of analytics useless for the near future.

Predictive analytics is used to answer, “What if”, and is more likely to be of use post Covid-19. It provides insight on understanding the future. It is used to predict the probability of what might happen and provides a business with insight which can be actionable. It is not 100% accurate.

In relation to Covid-19, economists have detailed several scenarios of how the economy may recover, due to the shape of its recovery “V”,” U”,” L” or “W”.

Predictive analytics can be used to model these scenarios and provide insight.

  1. Price Elasticity.
  2. Credit Worthiness of potential customers.
  3. Customer Lifetime Value.
  4. Id customers that are likely to abandon your service & churn.
  5. Targeted marketing campaigns; maximise profits & ROI.
  6. Optimise staff planning

Prescriptive analytics removes the guess work out of data analytics.

It is the final step in modern data processing. It takes the data we know, “predicts” what could happen and then “prescribes” the best way forward based on informed simulations.

  1. Manufacturing- Production sequencing and workforce scheduling.
  2. Customers- Determine which we should serve and how.
  3. Healthcare- Reduce and cost of hospital re-admissions.
  4. Education- Adaptive learning, automatic feedback, or actions.
  5. Auditors- From sample to continuous monitoring- lower margin of error.
  6. Investment opportunities- find consumer & market behavioural patterns.
  7. Supply Chain- Scheduling and delivery of the right products at the right time.

Numerous tools are used to develop this.

When implemented correctly, it can have a dramatic impact on and answer any question related to strategic, tactical, or operational decision making, directly impacting the bottom line.

It is essential for businesses to keep in mind that at each stage of their development there will be different benefits when embarking on an analytical transformation, some of which are:

  1. Find hidden knowledge, patterns & anomalies to reduce op costs, working capital and improve profits from efficiencies and identify new investment opportunities.
  2. Significant increase in ROI.
  3. Handle large volumes of data to create dashboards- removing nonvalue activities and creating meaningful reports for decisions to be made.
  4. Increase the efficiency & productivity of relevant professionals so they will be able to spend their time partnering with the business to change the dialogue, foster trust and add value.
  5. Improve data quality and remove bias from metrics to enable intelligent business decisions to be made.
  6. Provide innovative ways for smaller companies to survive when they cannot cut costs by headcount alone.
  7. Provide models to business leaders, such as price elasticity, churn, and customer lifetime value.
  8. Provide on-going support to either supply a dedicated service or additional support during a project.

Use of prediction & prescriptive analytics is on the increase and will become essential for staying competitive in an ever-changing world especially with the digital finance revolution gathering speed. As margins are squeezed it is going to become more critical to combine and leverage human intuition, professional skill and Data Science to drive performance, reduce operating costs, and increase the efficiency of busines to engage with the decision makers to enable them to make informed choices.

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