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Currently viewing the tag: "Machine Learning"

The Data Analytics Revolution is here. It is transforming how companies organize, operate, manage talent, and create value. In fact, advanced pic1 purpose-driven analyticsdata analytics is now a quintessential business matter. It is important for CEOs and top executives to be able to clearly articulate its purpose and translate it into action. Yet, this is not so.

CEOs and top executives are finding it difficult to articulate the clarity of purpose and act on it. It must not just stay in an Analytics department but must be embedded throughout the organization where the insights will be used. Leaders with strong intuition do not just become better equipped to kick the tires on their analytics efforts.  Leadership Development now calls for leaders to be capable of addressing many critical top management challenges. It now requires employing a range of tools, employing the right personnel, applying hard metrics, and asking hard questions.

Data Analytics is a means to an end. It is a discriminating tool for identifying and implementing a value-driving answer. It can unleash insights that could be the very core of your organization’s approach to improving performance. This, however, cannot be achieved if there is no clarity in the purpose of your data.

Data Analytics Revolution: Are We Ready?

The Data Analytics Revolution is transforming how companies organize, operate, manage talents, and create value. But are we ready for this? A number of companies are reaping major rewards from Data Analytics. But this is far from the norm. More CEOs and top executives are avoiding getting dragged into the esoteric weeds.

Data Analytics have complex methodologies and there is a sheer scale of data sets. Machine Learning is becoming increasingly more important. For us to be ready in the onset of Data Analytics Revolutions, we need to be capable of addressing many critical and complimentary top management challenges. We need to be able to ground even the highest analytical aspirations in traditional business principles and deploy a range of tools and people.

To be properly equipped on the proper use of Data Analytics, we just need to develop a mindset for Purpose-driven Analytics anchored on 4 guiding principles.

The 4 Guiding Principles of Purpose-driven Analytics

pic2 purpose driven analytics

  1. Ask Clear and Correct Questions. The first principle focuses on generating impact the soonest. Hence, precise questions are asked based on the company’s best-informed priorities. Here, clarity is essential.
  1.  Identify Small Changes for Big Impact. The second principle focuses on generating gains even on small improvements. There is a need to identify small points of difference to amplify and exploit because the smallest edge can make the biggest difference.
  1. Leverage Soft Data. The third principle focuses on getting quality insights and generating sharper conclusions. It is at this point wherein the use of softer inputs such as industry forecasts, predictions from product experts, and social media commentary are given more emphasis. Soft data is essential when trying to connect the dots between more exact inputs.
  1. Connect Separate Data Sets. The fourth principle focuses on capturing the untapped value. This principle emphasizes the need to combine sources of information to make sharper insights. When different data sets are examined, the greater is the probability that problems can easily be fixed.

From Learning to Doing: Connecting the Dots

It is not enough that organizations learn about Purpose-driven Analytics. One also needs to be able to put these into effective use. Companies undergoing Digital Transformation must take a multi-faceted approach to analyze data to minimize overwhelming complexity. There are 4 guiding principles for Purpose-driven Analytics implementation. Using these principles will facilitate the effective use of analytics and transform outputs into action.

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Robotics 2Disruptive technologies are helping companies automate work. Robotic Process Automation and Artificial Intelligence are taking up jobs which were in the past earmarked only for smart humans. Driver-less cars, automated check-in kiosks at airports, and autopilots steering the aircrafts are just few instances of how automation is transforming our world.

However, automation presents unique challenges that organizations need to identify and mitigate appropriately. These include costs associated with job losses; confidentiality of data; quality and safety risks stemming from automated processes; and regulatory implications.

Other critical factors to consider before investing in automation are adoption, pace of development of automation, and readiness of organizational leadership in redefining processes and roles to support automation.

The key question is how automation will impact our work in future. Should we anticipate benefits — e.g., efficiency gains and quality of life improvements — or dread further disruption of established business and job cuts?

Research by McKinsey suggests that Robotic Process Automation will impact 4 workplace areas the most:

  1. Workplace Activities
  2. (Re)definition of Work
  3. High-wage Jobs
  4. Creativity and Meaning

Now, let’s discuss the first two key areas in further detail.

Workplace Activities

Research findings (based on the US labor market data) reveal that the future does not likely hold complete automation of individual jobs, but rather automation of certain activities within specific occupations. The assumption that only routine, codifiable activities can be easily automated — and those that necessitate implicit knowledge will be unaffected — is misleading. Automation has already reached (or surpassed) the median level of human performance in some cases.

Capital or hardware-intensive industries — under stringent regulatory control — are slow and expensive to automate and need more time to reap return on investments. Whereas, the sectors where automation is mostly software based (e.g., financial services) may create value at a far lower cost and within rather shorter span of time.

(Re)definition of Work

The current level of automation can potentially transform a number of occupations to a certain level, but it requires redefinition of job roles and activities. Research reveals that only about 5% of occupations can be completely automated with the current level of technology.

In spite of this, automation can boost human productivity even in the highest paid occupations by taking care of repetitive daily tasks — e.g., analyzing paperwork, reports, data and evaluating applications based on criteria — and freeing up time for people to focus more on high value work that involves human emotions and creativity.

For instance, Automation and Machine Learning can automate diagnosis of common ailments, thereby enabling the doctors to concentrate more on acute or complicated problems. Likewise, lawyers can employ data mining tools to sift through piles of documentation to isolate the most relevant cases for their review.

Interested in learning more about the other key areas most impacted by Robotic Process Automation? You can download an editable PowerPoint on Impact of Robotic Process Automation here on the Flevy documents marketplace.

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