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How to Use Data Science to Make Faster and Better Decisions

Could it be possible to make your organisation twice as agile? When businesses behave intelligently, they use data science to facilitate a culture of continual, democratised decision-making, which can cut response times in half.

01 February 2022 • 5 min read

Photograph: Eugene Golovesov/Unsplash

Traditional organisations may make a decision sporadically, but intelligent organisations make decisions continuously, on all levels, including across the organisation’s official borders. Intelligent organisations also use data science to cut down the response time by as much as 50%, essentially doubling the organisation’s agility. In this article we outline the contours of the intelligent, data-driven organisation.

Managers often have to use 50% of the knowledge to make 100% of the decisions while being responsible for the results. Meanwhile, the volume of data keeps growing. Just dropping all of your structured and unstructured data into a data lake is usually not the right solution.

A dynamic environment demands not just an agile organisation, but decisive leaders and flexible employees.

Organisations are typically slow and reactive when it comes to making decisions. This isn’t always a bad thing, but a dynamic environment demands not just an agile organisation, but decisive leaders and flexible employees.

Intelligent organisations create an environment where employees’ work is data-driven. They develop a collective intelligence, make the right decisions faster, and continuously implement process improvements and innovation. They design algorithms for complex, repeating decisions, especially when it comes to operational decisions. They also think from an AI-first perspective.

So, organisational intelligence is about perceiving and responding to high-quality signals within the organisation and the environment faster and better, and processing data with the goal of continuously improving performance.

Highest values

Based on four concepts, organisations can measure how intelligent they are. These are the highest values that all CXOs should manage and strive for if they want to keep growing and improving.

  1. All-round vision: does your organisation have an all-round vision, and is it sensitive? Is management aware of what’s happening in its environment, with its competitors, in society, and in the workplace? Can they translate this into a clear vision and powerful mission?

  2. Analytics: how extensive is the organisation’s analytical capacity? How deep does it go? Is data from various structured and unstructured sources (automatically) combined and consistently analysed by managers and business analysts? How accurate is the predictive value of machine learning models?

  3. Agility: is the organisation agile? Can it quickly make decisions and respond to changes in the market in a timely and adequate fashion? Can it quickly develop new products and services and launch them flawlessly?

  4. Alignment: is the organisation capable of creating alignment between various departments, disciplines, and teams?

Data science is most effective when all disciplines actively work together in concert, which can release a lot of positive energy in your organisation.

The major ingredients of an intelligent, data-driven organisation

Data science is most effective when all disciplines actively work together in concert, which can release a lot of positive energy in your organisation. Continuously improving, innovating and refining or changing your strategy is driven by reliable and relevant data. The right decisions can be made quickly based on facts, whether they be strategic, tactical, or operational. Doing better than yesterday and better than your competitor, supported by professional data management, data science and algorithms. Add to that a culture that stimulates knowledge sharing, continuous improvement and innovation, and you have all the ingredients of an intelligent, data-driven organisation.

The future of data science

The data warehouse is still the beating heart of the intelligent organisation. But times are changing. The world of AI and data science is still changing rapidly. These are exciting times. Algorithms and (software) robots are penetrating the workplace and taking over tasks that used to be executed by people. The following developments are unmissable to anyone working on creating an intelligent, data-driven organisation:

  • Artificial intelligence enters the mainstream
    The number of AI success stories are beyond count at this point, and mainstream media is paying attention. The story of the winner of the Dutch BI & Data Science Award 2022, Pon’s Datalab, for instance, speaks for itself. Self-learning algorithms are increasingly making independent decisions and intruding on the private and public domain. Decisions made by governments, credit card companies, and banks can be completely based on algorithms.

  • Auto machine learning (AutoML) is taking off
    As many tasks that fall under the remit of data scientists are being picked up by machines without human interaction, frameworks and tools that enable users to develop their own machine learning models are on the way. For data scientists, developing deep learning models will be all that’s left, in the end.

An algorithm can easily replace 100 reports.

  • Report builders are on the brink of extinction
    We’ve already predicted that AI will make many report builders redundant. An algorithm can easily replace 100 reports. Why? Because reports are mainly about looking back; algorithms help you predict the future and prevent negative events. The more mistakes you prevent, the less valuable it is to look in the rear-view mirror. There’s a reason why the traditional market leaders in the reporting field (IBM Cognos and SAP BusinessObjects) are no longer on top of the world.

  • Data discovery, visualisation, and storytelling are hot
    These are becoming increasingly popular, appearing at the top of most trend-watching lists in 2022. The story behind the numbers has to come alive to convince the decision-makers of the facts, make decisions on a granular level, and secure the implementation of data science.

  • Good, scalable data infrastructure is crucial
    AI has entered the mainstream, data discovery is the norm, and the volume of data is growing exponentially. All these developments make high-quality, scalable data infrastructure (in the cloud) essential. Microsoft Azure, Google Cloud, and Amazon AWS are the market leaders in this field. IBM recently split itself into two public companies to focus on cloud computing and artificial intelligence.

  • Data quality is more crucial than ever
    Fortunately, many organisations have already realised that they can’t make reliable predictions using poor data. Reliable data has become a hygiene factor that directly impacts operations. Using low-quality data in AI applications is asking for trouble: it leads to impure algorithms that should not be taken into production. In the case of a traditional report, there’s always a human eye that can spot any errors. An algorithm lacks that human perspective and common sense, so bad data can go undetected with potentially disastrous results.

  • The CDO enters the boardroom
    Every data-driven organisation needs a Chief Data Officer (CDO) in the boardroom. While the CIO is often very cost-conscious, the CDO is more focused on value: how can data best be used to create value? More than 50% of all large organisations have a CDO according to Forbes, a percentage that is sure to increase. And a 2018 study by McKinsey shows that organisations with a CDO are almost twice as likely to complete a successful digital transformation.

  • Data governance gains a place of prominence
    Thanks to (digital) developments like AI, data science, privacy legislation, and blockchain, more and more organisations are realising the importance of ‘putting their data house in order’. From data input to big data creation, from master data to metadata, from data acquisition to data selling, and from data structure to data culture. The difference between big data and normal data will fade over time. The key is to properly manage, clean, structure, and use data.

Autonomous, entrepreneurial employees, well-crafted and data-driven strategies, and agile structures shape the contours of the intelligent organisation. That requires sublime integration of systems based on standards, open cultures, a professional and first-rate organisation, and proactively sharing information, knowledge, responsibilities and results. With leaders fully equipped to make decisions fast, and employees given flexibility and influence, the intelligent, data-driven organisation has the agility to cope – and thrive – amid the dynamism of today’s business environment.

Artificial intelligence Data strategy & BI Operational excellence Shaping culture

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