Accelerate your Business with Machine Learning

Business leaders already clearly perceive how the methodologies and technologies associated with Big Data and Machine Learning will accelerate opportunities and define which companies will survive (and which will not) in the next 50 years. However, in Latin America it seems that the adoption of these issues is diffuse and unclear in corporate environments. A strong change of work culture, a serious plan of professional training (or recruitment of experts) and discipline to execute the plans (even in times of crisis) can be the key for companies in Latin America to accelerate their business with Machine Learning.

I have been working for several years in the Professional Services team inside Google Cloud, based in California. Sitting in the high tech center of the world gives me visibility into how different industries and the most representative companies in each sector face business challenges. And given my Latin American origin I maintain connection (both professional and personal) with leaders and executives from Latin America who often come with the famous comment

"I would like my company to advance with Machine Learning as others do it in the United States".

It is naive to think that organizations that have been working with the same culture and technology for more than 30 years can be successful in executing a transformation program without an appropriate preparation. Many leaders have exposed their organizations to these processes without taking precautions. In this op-ed my intention is to provide clarity on the concepts and key aspects that every executive must consider to accelerate the business of their organization.

To summarize, this is a list that every executive can use to validate a digital transformation program focused on Machine Learning. A more detailed explanation can be found below:

• Executive decision to invest in Machine Learning as a differentiation for the business in the long term
• Work culture focused on data. Every decision must be based on data points. Every project must consider aspects of Data Analytics from the initial moment (data-driven vs hierarchy driven)
• Recruit and onboard external leaders with relevant experience that can fit in the org. work culture
• Execute an internal training plan cross organization (IT and Business areas) with focus on Cloud, Data Analytics and Business Intelligence
• Define ambitious and aggressive goals, but plan short project stages with clear metrics to measure the success of each stage.
• Work together with technology partners with strong expertise in Cloud and ML.

How to approach Machine Learning in an organization

The first step, the work culture

Embark fully on the journey of Machine Learning can generate returns of high value but also requires heavy investments in what is usually a trip of several years. These initiatives require a clear commitment from CEOs and the board to achieve long-term objectives. It requires focus and decision to turn organizations into teams that make decisions based on business data. While the enterprises called "digital natives" (companies born and raised from the Internet) have been created for the use of "analytics" (a term used as a synonym for continuous measurement of business metrics automated by technology), the "legacy" companies have to do the hard work of changing their systems. And more importantly, they must adapt the organization so that decision making is based on data. This must become part of the "culture" of the company. Executives must learn to bring data to every conversation. And in every initiative from the zero moment one should think about the "KPIs" and the systems that will allow the monitoring and democratization of information to break the disconnected data silos. Data & Analytics must be part of the "core" vision of the organization. The business processes, the necessary infrastructure and human talent must be aligned.

Recruit experienced leaders, empower everyone

Many companies find challenges to develop talent. Professionals in organizations focused on digital transformation should be able to accelerate the processes to develop solutions in the Cloud. The technology environment in the Cloud is the natural environment for Data Analytics and Machine Learning to scale without large financial investments that would leave the vast majority of organizations without a chance to compete.

To achieve this, companies in Latin America must invest in hiring and training Cloud experts. And develop these capabilities even outside of the traditional IT organization. Business areas such as Marketing, Sales and Human Resources must have resources in their organizations that can be the bridge between technology platforms and business needs. These “transformation champions” need to understand the intrinsic nature of the data and define a strategy including the datasets that the business will require to make decisions. Technology for these reps. should be a common vocabulary to enable interactions with IT leaders.

A fast digital transformation can only be executed at high speed when the existing staff is trained and has access to collaboration tools. A good practice is to set up digital innovation labs (or challenges) where there is a team responsible for "reimagining" the organization living 100% on Cloud platforms. The use of design thinking techniques across the organization can accelerate the process. Recruiting and onboarding leaders with Cloud experience facilitate these efforts in an optimal way.

Consistency over time, key to success

Any coach or sports director could describe it in a very simple way. The great athletes of history do not become idols for the second in which they execute a great play, but for the years invested in continuous training and improvement. Translating this into the corporate environment you could say that there is no magic for success. It requires a lot of discipline and focus so that the objectives translate into progress over time. This does not mean working in magnanimous plans of 10-year projects. Following Google example, the vision has to be oriented to 10X goals beyond where the organization is today. But the way in which programs are drawn and defined is partitioning these mega objectives into multiple and small steps, each of them with metrics and mechanisms to measure their success.

If you are still reading this note you will realize that it is not trivial to accelerate the process of an organization to develop business solutions with Machine Learning. In addition to everything already described it is fundamental to have technology partners and systems integrators that act as "trusted advisors" to facilitate the process. Organization leaders supported by an agile and trained partner will be able to define challenging objectives and stages to achieve them in the short term. The work culture of the organization will be reinvented and always defined based on data. And with the recommendations and best practices of the chosen partners it will be possible to advance securely in the transformation of the business based on Big Data and Machine Learning.