This is the first in a series of posts by Pavandeep Kalra, Director of Data Science, and Ilan Reiter, Principal Data Science Manager, where they discuss business transformation through analytics and how organizations can run successful analytics pilots and build a thriving data science practice.
We all know the story of how, in just the last few years, data from myriads of different sources have literally exploded, and, along with it, there’s also been the dramatic shift towards massive on-demand storage and computing in public clouds such as Azure and AWS. What’s more, the tools and applications available for organizations to build sophisticated data analytics solutions have become simpler and much more accessible too.
With just an internet-connected browser, the Cortana Analytics Suite, for instance, gives you the ability to ingest enormous volumes of data in real time, store exabytes of unstructured or structured data, orchestrate complex data flows, create operationalized machine learning models almost trivially using drag and drop, and easily take advantage of rich visualization and dashboarding capabilities. You can even use sophisticated perceptual APIs for things such as face or speech recognition to create solutions that would have been unthinkable just a few years ago.
Questions on Customers Minds
Given the rapid evolution of this field, customers typically need guidance on how to apply the latest analytics techniques to address their business needs or to pursue new opportunities. The C-suite executives we meet have many questions regarding data analytics, for instance:
- How can analytics help me transform my business, better manage costs, and drive greater operational excellence?
- How can analytics help me better market to my customers, drive more personalized messaging, drive better targeting, improve customer engagement and propel my sales?
- How can I better manage risk across my business using the latest analytics techniques?
- How can analytics help us innovate and become thought leaders in our industry?
We have been fortunate enough to work with a multitude of customers on a range of analytics projects, and, in typical projects, we are now able to create end-to-end operationalized analytics systems – in production – within a matter of a couple of months. We are talking here about sophisticated customer scenarios that address complex challenges faced by our customers. In fact, in many cases, customers have been grappling with these problems for years, even decades.
Here are a few examples of the kinds of customer issues we have addressed in some of our recent work:
- Personalize the offers shown to consumers on our website in real-time, using their click history of the last 10 minutes.
- Tell me when my equipment will fail, so I can optimize workforce scheduling.
- Forecast my energy consumption with high accuracy, so I can balance the energy grid in real time.
Hard problems such as these and many more like them are now readily solved using the different tools in our quiver.
Although a few companies end up becoming poster children for how to use analytics to change the game and perhaps even transform an industry, others end up tentatively doing a pilot project or two, but then fail to move things much farther. For every ThyssenKrupp that’s using analytics to drive transformation in the elevator industry or Dartmouth Hitchcock that is revolutionizing personalized healthcare, there are others who are falling behind in their effort to use analytics to create sustainable business impact. It is not uncommon for companies in these two different camps to even be using the very same technology.
The question that arises, then, is – why does that happen?
Why are some companies much more successful than others in driving analytics-based business transformation? Based on our experience and observations, we’ve boiled this down to a few key factors:
- Clearly articulated vision and purpose. Driving transformation requires that you have a well-defined and clearly articulated purpose and vision for what the organization is looking to accomplish. It often requires the support of a C-level executive to take that vision and drive it through the different parts of a business. Where there is a clear purpose, vision and sponsorship, the taste of initial successes or early wins spurs further experimentation and exploration, often resulting in a domino effect of positive change.
- Culture of empowerment and experimentation. Even where there is a clearly articulated purpose, that alone often doesn’t lead to successful business transformation. An important obstacle in many organizations is the fact that employees just aren’t empowered enough to bring about change. The culture of the firm may not be one that encourages experimentation or risk-taking. However, given the generational shift in the workforce that is currently under way, with younger employees seeking a ‘purpose’ more than a ‘job’ or a ‘paycheck’, having an empowered workforce helps engage your employees and gets them actively involved in contributing towards a common goal.
- Start small and get online quickly. Starting off by doing a small analytics pilot, something that is meaningful, works end-to-end and is fully operationalized, often helps to illustrate the value of analytics in to the rest of the organization. For example, if sales associates in one department find that they are spending less time cold calling and more time closing deals because of an improved “lead scoring” pilot, others in sales roles will be sold on the value of analytics in helping them close deals and make more money for themselves and their firm. Small online pilots are the quickest way to test your hypotheses in the real world and see which ones stick. Quick wins help promote an analytics -driven culture across your organization. Smaller wins will often snowball into a bigger transformation of the business as a whole.
- Collect the right data. Although companies are collecting petabytes of data, the key question is: is it the right data? Do you have relevant data for the problems you are trying to solve? Let’s take Predictive Maintenance, a technique used to predict when an in-service machine will fail, allowing for its maintenance to be planned well in advance. As it turns out, this is a very broad area with a variety of end goals, such as predicting root causes of failure, which parts will need replacement and when, providing maintenance recommendations after the failure happens, etc. Many customers who are attempting predictive maintenance have piles of data available from all sorts of sensors and systems. But, too often, customers do not have enough data about their failure history and that makes it is very difficult to do predictive maintenance – after all, models need to be trained on such failure history data in order to predict future failure incidents. So, while it’s important to lay out the vision, purpose and scope of any analytics projects you wish to take on, it is absolutely critical that you start off by gathering the right data.
Advantages of a Modern Cloud-Based Analytics Platform
Assuming you have all the right ingredients above, including the right employee culture, you still need to have the right technology platform in place – one that boosts your employees’ productivity and helps them innovate and iterate rapidly. A modern cloud analytics environment will make it super easy to collect data, analyze, experiment and quickly put things into production with a targeted set of customers. This sort of capability is becoming a must-have for data-driven organizations, large and small.
A modern platform like Cortana Analytics provides the right foundation for your organization to successfully transform your business through the power of data and analytics.
Without such a platform, employees will find it hard to rapidly iterate over many experiments, learn quickly from their failures and successes, and discover interesting actionable insights from your data. Without the right culture, infrastructure and tools, your organization will eventually find itself lagging behind nimbler competitors.
C-suite executives today need to think about these and related organizational and technology considerations when it comes to their adoption of data analytics. The ability to successfully navigate through these issues could very well determine the viability and longevity of their business.
In future posts in this series, we will discuss how to successfully run analytics pilots and build a thriving data science practice in your organization – stay tuned.
Pavandeep & Ilan
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