Success Stories


Success Story

Business analysts, data scientists, authors and bloggers sometimes describe data mining and analytics as “finding the story in your data.” It’s a good metaphor although I want to extend the notion from a single story that can be told to multiple stories because there are actually a multitude of stories in your data. Each story can be told based on the types of data your organization collects and by the analytics applied. Stories in your data can be about:

  • Your operations, including specific facilities, machinery and assets
  • Your sales activity
  • Your vendors and suppliers
  • Situations and events that affect or impact your organization
  • Your customers, their buying journey, their interaction with your organization and its website
  • If your organization has connected to the Internet of Things (IoT), then stories can also be told about the things, their environments, and their end-users including how they use and interaction with your products

Analytics can tell stories in many different ways. Which is to say that the types of analytics that you use will tell a different story. Knowing the particular type of stories you are interested in and want to benefit from most influence your criteria for choosing analytics. One type of storytelling, let’s call it situational intelligence, is a comprehensive fact-based story about what happened with details about where, when, why and how. Such stories contribute to understanding situations and root cause(s) that can drive decisions and actions to prevent future occurrences. Situational intelligence stories are therefore extremely valuable for understanding situations so you can take prompt and appropriate action to achieve success.

Your stories become enriched with more useful and actionable detail as more analytics are applied. The following brief high-level examples illustrate this point. Analytics enrich stories by identifying outliers, groups, patterns, and top and bottom performers. Forward-looking analytics (aka predictive analytics) enrich stories by foretelling things such as demand, foretelling what is most likely to happen. Other types of analytics identify and describe relationships between entities and enrich the stories with insights into ripple effects. Analyzing relationships enables telling detailed stories about the magnitude of situations – what else is or might be affected and to what extent. Analytics also is able to create a successful story ending by considering all possible outcomes and then choosing the best or optimal outcome. It should now be clear how multiple different analytics can be applied and combined to tell very rich and detailed stories. Actionable stories in fact, because the stories in your data drive investigation, decisions, actions and the best possible outcomes.

In addition to applying and combining different analytics to research the stories to compose compelling content (metaphorically speaking), there is another primary method of enriching stories – with data. After all, the data that is available to the analytics is the foundation and critical component. It is generally the case that the more data available, the more comprehensive of a story can be composed. And not just more data from the same source (e.g., 5 years of historical data versus 2 years) but data from complementary and supplementary sources. Your own data sources can and should include data streams from your website and/or your integration with the Internet of Things (IoT). You can and should augment your data from relevant external sources such as web services that provide weather, traffic, spot market prices, exchange rates, etc. Using multitude sources of data (referred to as broad data) is similar to how a journalist doing research for a story will seek and use all practical and relevant sources of information to tell the most accurate and complete story possible.

Let’s take this analogy one step further – consider a story about a customer’s use of a product that your organization produces and sells. The [usage] story can be told just by data captured from the customer’s interaction with your organization. If the customer is using a web-based service and/or an IoT device, your organization can receive an ongoing stream of data. If all of the data about the customer is fused with data from another system, such as the ecommerce system that captures aspects of the customer’s buying journey, then the resultant story becomes richer. Your organization can use such stories to understand and predict how the customer might acquire new products, what else the customer might need or want to buy, and how they might interact with your organization in the future. A more compelling story – a success story; one that can drive personalized experiences and sales offers that have a high likelihood of achieving customer retention and increased revenue.

More and more organizations are applying analytics to gain a competitive advantage using the actionable stories in their data. You too should embrace analytics to ensure your own success stories.


Are You Ready For Autonomous Freight Trucks?


truck convoy web

Recently a group of freight trucks drove across Europe autonomously. Daimler is now selling new semis with autonomous driving features. A Silicon Valley start-up wants to sell equipment to retrofit existing freight trucks for autonomous driving.

Are you ready to share the highways with autonomous trucks?

Autonomous trucks are not driver-less trucks. Trucks still need drivers to handle city driving, parking, refueling, delivery paperwork and many other aspects of freight handling. It’s during those long, boring stretches of highway driving where autonomous systems relieve drivers of some of the dullest and most dangerous work.

The work is dull enough that there’s currently a driver shortage. The American Trucking Associations report that the United States needs about 50,000 more truck drivers. Autonomous systems might help make the job more appealing, and maybe even make today’s drivers more productive. Both outcomes would help with the driver shortage.

Autonomous trucks are designed to have fewer accidents, more predictable performance, and better fuel consumption. One way these trucks conserve fuel is by lining up in platoons to reduce wind resistance.

The trucks use a combination of video cameras and radar to sense the situation around them. That video and radar data can be enriched with other information about weather, terrain, destination and cargo, then analyzed to optimize routes, schedules, fuel consumption and more. The analysis may suggest, for example, that the truck is ahead of schedule and slow the vehicle to arrive on time and conserve fuel in the process.

If your company owns a fleet of such smart trucks, then you might benefit from analysis spanning groups of trucks or your entire fleet. For instance, could you change schedules and routes so that more of your trucks travel in platoons to save fuel? Will the suggested changes to routes and schedules still meet your shipping agreements or delivery commitments?

For years as air travelers, we’ve ridden in planes with autonomous piloting and enjoyed it as the safest form of transportation. But when we’re operating our cars alongside autonomous trucks, does our attitude change? Personally, I’m not sure how I’ll feel passing, or being passed by, a seeming wall of platooned trucks rolling down the road.

What are your thoughts?

( Image: whitestar1955 / 123RF Stock Photo )


Analytics and Vegetation


vegetation blog

Electric utilities, cable operators, pipeline companies, railroad, municipalities—all will tell you that it’s a jungle out there. Vegetation has a way of interacting with and interrupting the operations of technologically sophisticated and complicated networks. Even your wireless communication networks are not immune to the impacts of vegetation.

Vegetation causes trouble in several ways:

  • Falling onto assets, such as trees falling across roads, damaging them or rendering them unusable
  • Growing into assets, such as roots growing into sewer lines, lowering their performance or making them fail
  • Making contact with assets and causing malfunctions, such as tree limbs touching power lines and causing power outages or sparking fires
  • Allowing wildlife to contact assets and cause equipment failure, such as bushes helping squirrels enter substations and disrupt power operations
  • Obstructing rights of way such as roads, bridges, tunnels and waterways, for example reeds and seaweed clogging ship channels

Similarly, the lack of vegetation can also be a problem. Slopes that have lost their vegetation due to wildfires during times of drought become prone to erosion and landslides when rains finally return. If these areas are adjacent to roads, waterways, power lines, pipelines or other assets that you own or operate, sudden ground movement from erosion or landslide could damage your equipment or block access.

An asset-intensive organization can spend millions of dollar per year on spraying, trimming, pruning, removing and replanting vegetation. Its labor intensive work with costs that add up quickly. When you experience an unplanned event related to vegetation—tree fall, land slide, brush fire—your emergency costs pile up while services are interrupted.

There are vegetation management systems available to organizations today. Maybe you use one. These mainly target the management of scheduled activities, routes and workers. They are useful, and can be augmented to be more valuable by integrating intelligence about actual and potential problems into the scheduling of trim activity. Advanced analytics will identify the areas most in need for trimming or other management and also optimize overall crew schedules so that your vegetation management processes and costs to improve reliability and safety and lower operational costs.

A situational intelligence approach to understanding your vegetation challenges and potential problems maps vegetation’s proximity to your networks, predicts how vegetation will grow and interact with those networks over time, and prioritizes the geographic locations and network sections most susceptible to vegetation problems.

Data about tree and plant species, microclimates, past and future rain fall, time of year, and other variables informs growth models that improve your vegetation management schedules. By applying analytics to this data, you can prioritize your work more effectively to address true problem areas and not just the next assignment in the vegetation management cycle.

By working differently, working smarter, you can optimize your vegetation operations budget and make your networks and assets more reliable.

Copyright: alephcomo / 123RF Stock Photo


Why We Need Situational Intelligence, Part 3


In Part I and Part 2 of this series I addressed why situational intelligence is a natural and essential method of decision-making that is especially apropos for real-time business operations. Inherent in my argument is an altruistic belief that people make the best decisions and take the best actions with the information at hand. That is the crux of the matter – the information at hand and how accurate and actionable it is. What information is available to decision makers? Does it contain insights? Is it current? Is it clear or is interpretation and/or further analysis necessary before the information is actionable? Is it reliable? How comprehensive is the it? Correspondingly, how much uncertainty shrouds the information, the decision, the action and the outcome? What are the risks of making a bad decision (including no decision)?

Ideally the answers to the preceding rhetorical questions should all be encouraging. But how can these attributes of data and insights for decision-making be assured, especially when decisions are made by different people, when decisions needed are for unplanned situations, and when timeliness is important? Systematized decision-making aided by technology-generated intelligence is a way to assure that accurate insights are derived from data and actionable by decision makers. As discussed in the preceding blogs (and other blogs too), advanced analytics and visual analytics are essential building blocks for analytics that support operational decision-making. Data must be transformed into insights and intelligence. The insights must also be transformed so they are readily comprehended at-a-glance and are actionable.

Another key consideration is having a broad composition of data for analysis. The more data from relevant sources within the enterprise, from the IoT and from external sources, the more insights can be derived by analytics. Accessing external data enriches intra-enterprise data sources with relevant context that is useful when decision makers require supplemental information, such as when insights brought forward to decision makers is not immediately actionable. In such cases further discovery helps decision makers gain the needed understandings and confidence to make a decision. This is where additional data sources and the corresponding added context facilitates interactive data exploration so that decision makers can make timely and favorable decisions. Sources and types of external data include: weather, traffic, news, spot market prices and social media.

Having live connections to data sources ensures that decisions are made using the most up-to-date data, and also enables interactive exploration of underlying data to deeply understand and resolve complex multifaceted situations. A single system that maintains live connections to data sources yields another benefit – it helps organizations bridge their data silos and unify their data assets.

Here at the end of this blog series, situational intelligence now sounds easy, and somewhat obvious too – connect to relevant data sources, apply analytics, make the resulting insights and underlying data available to decision makers with intuitive visualizations so they can consistently make the best possible decision in any situation. If you use an off-the-shelf solution to implement situational intelligence, getting started is also relatively simple. Decide for yourself. What does your situation require?

If you have experiences, thoughts, opinions on this topic, please comment and share them.