Helping Data Driven Companies Advance to Artificial Intelligence

Everyone is talking about artificial intelligence (AI) and machine learning these days. This is not just of strategic relevance for companies the likes of Google, Apple, Amazon, Facebook or Salesforce.com. AI is now a term that all companies should be familiarizing themselves with (if they’re not already) because it will have a profound impact on their business in the near future. We have already witnessed vehicles operating autonomously and a proliferation of robotic counterparts and automated means for accomplishing a variety of tasks, which has all given rise to a flurry of people claiming that the AI revolution is upon us.

What is Driving This Next Wave of Change?

It is interesting to observe that very little has changed as far as the basic theories of Artificial Intelligence are concerned. Overall we still have the same approaches and concepts in the field of machine learning as we have had for decades. Concepts such as artificial neural networks, back propagation, support vector machines, Bayesian networks or hidden Markov models have all been developed in the last century.

So, it is even more important than ever to ask why now is the right time for your business to take action on devising a plan for implementing AI effectively within your organization.

As far as I am concerned, the answer to the successful utilization of AI in any business is all about the availability of big data and a company’s IT infrastructure. The idea that data is a valuable commodity has long since been established. More and more companies are now basing their business models on data and analytics. We are living in the age of data driven organizations. When it comes to infrastructure, the cloud with its great scalability has become firmly established: resources are available at the touch of a button to scale to the extent required, and can be invoiced according to use—saving cost.  

Nearly all companies have big data, but not all of them have learned how to effectively leverage it. Before any meaningful business decisions can be made using data, it first needs to be collected and integrate data from a broad variety of sources, “cleansened“ to ensure it’s consistent and accurate, and rapidly moved to where it’s needed for real-time decision making. For example, data from a medical imaging process without image processing is still just plain binary data. The same applies to an online shop’s click-streams or the operating data from a machine. Data can only lead to relevant outcomes once it has been evaluated.  

Artificial intelligence takes a significant step forward in terms of enabling real-time decision making by incorporating the use of machine learning.  Machines can learn a model based on data which enables them to make decisions faster using new data received. In its spectacular performance on Jeopardy, Watson gave its answers all by itself (even though the answers were actually questions here). There are many other situations where machines making decisions on their own have far-reaching consequences: for example, when someone is recommended as a dating partner (with the consequence that a machine contributes to decisions about life-long relationships), or when a machine determines who is creditworthy (with the dramatic consequences this can have, e.g. being able to afford a house), or when a machine predicts potential criminal activities. Many such smaller machine decisions already have an impact on our everyday lives; i.e. determining who looks at what on Facebook, who gets which product-suggestions from Amazon;  who gets which adverts from Google; who must pay which price for a flight; or gets which answers from Siri.

If you look at things from a different angle, you will see the opportunities which can be opened up by using artificial intelligence processes and especially by using machine learning: complex systems learn from experience (historical data) and make decisions more cleverly on their own to determine outcomes such as how relevant is a lead? Who should we address with which campaign? When should a technician service a system? When might an existing customer consider moving to the competition or when might a member of staff leave the company? Which information is most useful for my user right now vs. in six months, and which services should we recommend?

As noted above, if you’ve not done so already, now is the time for your business to take action on devising a plan for implementing AI effectively within your organization.

My recommendation is very simple: AI processes will be changing our lives to an even further extent than what we can foresee today. The same applies to business models and the rules of competition. Anyone who has their data under control today, will be ready to take the next step tomorrow. But those who do not start to become data-driven organizations today, will miss out completely when the next step needs to be taken. So what steps should companies—who aren’t yet data driven—take in order to become data driven?  We will address this topic in detail in part II of this blog posting.

About the author Dr. Gero Presser

Dr. Gero Presser is a co-founder and managing partner of Quinscape GmbH in Dortmund. Quinscape has positioned itself on the German market as a leading system integrator for the Talend, Jaspersoft/Spotfire, Kony and Intrexx platforms and, with their 100 members of staff, they take care of renowned customers including SMEs, large corporations and the public sector. 

Gero Presser did his doctorate in decision-making theory in the field of artificial intelligence and at Quinscape he is responsible for setting up the business field of Business Intelligence with a focus on analytics and integration.

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I suggest you to try Long Path Tool program

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I suggest you to try Long Path Tool program

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