By Patrick McDonald,
Chief Architect, Advanced Analytics at Clarity Insights. Patrick brings 23 years of experience to over 50 data science and advanced analytics projects and has delivered $4.4 billion to client bottom lines.

As market research suggests, your strategy  for data and analytics is critical to your business strategy and corporate priorities. Business leaders should be asking themselves, “What will happen if we don’t have a data and analytics strategy?” This is no longer optional. Businesses that are slow to adopt one will pay a price in the marketplace. Will you thrive, survive or struggle to stay alive as competitors gain the insights-driven advantage? A Gartner report on data analytics trends, which describes momentum for advanced capabilities and continuous intelligence, demonstrates how analytics-driven decision-making is now attainable for most organizations if they’re ready to accept the challenge.

The stakes have never been higher

Research from Microsoft, reported on CIO.com, reveals that 80% of business executives believe digital transformation will affect their industries. But let’s be clear – it already is. Nearly half of them believed their traditional business models will be obsolete by 2020. The cringe-worthy stat that followed is that a very high percentage of businesses are not ready to compete in this new data-driven era. Industry research further shares that a remarkable 72% of C-level executives believe that their companies have yet to build a data culture and 52% feel they are not competitively leveraging their existing data and analytics approach. This means those organizations in the bottom half of the curve who haven’t made innovation and continued intelligence a board room priority and an organizational objective are headed towards the likes of Sears, Nokia and Yahoo.

 


 

2019 Market Guide for Data and Analytics Service Providers

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No more excuses – innovate, evolve or be left behind

Evolution and business transformation are achievable for those who are willing to focus and invest in what can differentiate them in the market. For example, Gartner’s recent report, “Top 10 Data and Analytics Technology Trends That Will Change Your Business,” predicts that half of major new business systems will incorporate continuous intelligence by 2022. However, it’s one of the most relevant trends for companies undertaking digital transformation. It may include augmented data analytics and event stream processing but also the innovative approach to drive better transactional decision making in real time. This latter capability is critical for companies that want to leverage the Internet of Things (IoT) for transformation. For example, IoT sensors, smart tracking and customer mapping can generate streams of experience data like the wait times and number of people waiting in line at a store throughout the day, across multiple locations.

Business systems used to generate insights based on real-time (or near-real-time), continuous ingestion of data, some leveraging machine learning (ML), are equipped to make proactive suggestions based on business rules or optimized predictive models. For instance, if the rule says that no customer should wait in line for more than two minutes, a system could review IoT line sensor data streams and alert store managers about line lengths as the lines are forming; an optimized model might look at multiple factors and determine in real time the wait time threshold resulting in the optimal business outcome.

Real time analytics can also apply to unstructured data like social media postings by ingesting huge volumes of social media data and analyzing it for brand sentiment or up-to-the-minute issues. What if people waiting in line start posting comments on Twitter like “#SlowLines.”

What should managers of this business do about the long line complaints? Should they take action and offer customers a real time incentive to wait longer or ignore the whole thing. Under- and over-reacting are both problematic moves. A support function enabled by real-time analytics to drive real-time decision-making can render a recommendation after interpreting customer sentiments and measuring them across a broad sample. It can determine if there’s is a real problem, a momentary issue or a one-off complaint.

You’ve got this

The good news is that there are organizations that are incredibly successful at implementing this level of advanced analytics and innovation best practices. The bad news… there are already organizations that are incredibly successful at this level of advanced analytics and innovation best practices. If it is achievable and attainable, then it’s time to start taking action and evolve or risk being left behind. 

You’re not a one-person solo artist though – true innovation isn’t done in a silo, nor on the individual level. Stakeholders across the organization need to understand the imperative and what it can do for them. Start with the highest level of understanding of what the business can do with better data and analytics, with more innovation and with an attitude committed to continuous evolution. It’s a series of thinking and listening exercises. 

Start with this… 

  1. “What is the main issue we’re facing?” – If the answer is lost revenue, the next question becomes…
  2. “What is the main contributor in the revenue decline?” – If the answer is lost customers, the next question is…
  3. “Why are customers no longer loyal?” – If the answer is poor customer experience, now you have somewhere to focus!
  4. Start answering questions around what is standing in your way and what can be done to enable better action. “If customer wait times are what is causing a bad experience, what can be done to monitor that?”
  5. If we had access to real-time data about customer wait times, what would that allow us to do and how could we improve customer experience with those insights?”

From there, adoption can be incremental. It’s about building a program around addressing real business challenges and connecting it to what can be done leveraging data and analytics. Word of warning though, data analytics projects can get into trouble by being overly ambitious early in the life of the program. Pilots and early “wins” are a great way to build proof of concepts with demonstrable results. 

The path to a data and analytics strategy that supports business strategy and transformation may not be intuitively obvious, but you’re not facing that journey alone. We’ve helped many organizations establish a framework and foundation leveraging analytics and insights to go from survive… to thrive. Let's talk.

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