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One of the key milestones on the Digital Journey starting with a Digital Strategy, Digital Transformation and then sustaining on Digital Innovation is the point where the enterprise reaches a point of data maturity powered by a single, organization wide, consistent version of the truth including the state of the customers and the state of the business and the state of the employees. This point is critical as it becomes the launchpad for several, forward looking initiatives including Artificial Intelligence, ChatBots, Blockchain etc. "Complete" A Complete version of the truth ensures that the following criteria is met: Entity Pivot The key entities that need to be tracked to generate a complete, comprehensive version of the truth are the following Employee Employees, regardless of customer facing or not, need to be understood including where they excel vs. struggle and where their struggle impact the customer experience. Key information about employees that should be tracked is what the employees are working on, how productive they are and how often they introduce delay and errors in business processes. Business Business visibility requires that the enterprise be able to track key metrics such as customer lifetime value, customer attribution, customer acquisition cost and customer satisfaction. In addition, the stage of the customer ranging from prospect to commit to paying customer to abandoned needs to be tracked. In addition, the customer's quality of service and experience needs to be tracked and understood. Customer The most critical of the three is the understanding of the customer. Customer KPIs have a direct impact on and are completely impacted by the Business and Employee KPIs. It is extremely important to understand how customers are searching for, discovering, learning, understanding, using and continuing to use the product and services delivered by the enterprise. In addition, it is important to understand what capabilities drive what kin

Enterprises across the board have a lot of untapped potential in their data. The data is not only relevant and useful within the enterprise but can be a valuable source of insights for the enterprise partners and customers. In some cases, the value of this data can be high that partners and customers are willing to pay extra to get access to this information at a certain fidelity, freshness or scope. Enterprises that do not have a clear and coherent monetization strategy are leaving money on the table. In addition, they stand to lose customers to competitors who gain the first movers advantage by addressing this market need. The Value of Data The first step in determining a monetization strategy is an audit of the enterprise data assets and a determination of the customers who are interesting and willing to pay a premium for access to this data. The Value of Data is proportional to the following: Freshness The more "fresh" a dataset is higher its value typically. This is because there is an advantage in the early visibility provided by first access to new information. 'Freshness' is defined the latency between the creation of data and the delivery of the data to the consumer. Consumers of data will pay a premium for fresh data if it fits into their decision and action strategy. Fidelity Higher the "fidelity" of data i.e. how much detail a particular data point carries also increases the value of the data in the eyes of the data consumer. Higher fidelity data offers more information and detail enabling the consumer to design highly valuable analysis that leverages the additional details offering a deeper insight into the situation at the present or historically. Raw The more "raw" a data set, higher its value as it can support a much larger set of analysis scenarios that a processed data set could support. Data sets that are aggregated, sampled, filtered or transformed can have a lower value as they can severely limit the type of analysis. Raw

Sentiment, Sentiment Analysis, Sentiment Tracking have become a hot topic with multiple 'AI' startups focussing on providing sentiment driven insights to enterprises. The number of such startups points to the potential that enterprises see in Sentiment analysis and the impact it has on how the enterprise plans, operates, executes and delivers value. Sentiment Analysis is process of extracting sentiment (emotion or feelings) captured in signals that are embedded in various types of media such as print, text, audio, video, images etc. For example, if a reporter submits a report on a particular enterprise, the sentiment embedded in the article can point to how excited, worried, upbeat or impassive they might are about the enterprise. This sentiment can be then used by the enterprise to understand the perception about the enterprise that the external market carries and whether that perception is improving, degrading or staying unchanged. This insight can be used by the enterprise to improve their go to market plans, change their PR strategy or even go deeper and change their product strategy. Sentiment Analysis Is Not New The tracking, measurement and use of Sentiment is not a new scenario. Enterprises have been leveraging the output of sentiment analysis for a long time. User surveys, focus groups, market research, customer interviews etc. are all examples of generating data to perform and track sentiment. Similarly, influencer marketing through association with influencers or events or organizations with a certain perception or sentiment associated with them is a common technique to improve the enterprise's own sentiment. Sentiment Analysis and strategizing based on the analysis is a common and required function for any enterprise. Sentiment Analysis Using Artificial Intelligence With the advent of Artificial Intelligence (AI), enterprises now have another technique in their kitty to understand how they are perceived in the market and how that perception i

There's no doubt about it, we're surrounded by technology and submit to an awful lot of data collection in return for the convenience factor. Even those of us who consider ourselves tech-savvy and try to keep track of our digital privacy can slip up from time to time. Here are five privacy risks you may not know about and five solutions. Incognito mode is not secure, or private A common assumption is that using a browser's incognito mode means any activity is untracked and hidden. That's not the case, incognito mode merely means your search history is not kept with your standard search history. Solution: VPN encryption Use a VPN to secure connections and effectively hide your internet activity, including your IP address. Google is tracking your purchases It may produce the world's most powerful browser, but Google has a lot to answer for when it comes to online privacy. The tech giant's email client even retains your purchase history. Solution: Stop using Chrome and Gmail The only real way to avoid Google's long and all-pervasive reach is to avoid all of their products. Ditch Chrome, Gmail, Docs, Sheets, and everything Google produced. If that's a step too far for you, try and delete your purchase history (https://myaccount.google.com/purchases) although, that's easier said than done. Free VPNs sell your data Users may think they're being clever and retaining their privacy by using a VPN at all times. However, if you're using a free VPN you're risking your data being used as a commodity. Many free VPNs collect then sell your information. Solution: Switch to a secure paid service Using a paid and trustworthy VPN can guarantee the security of your information. Risk: Google "skim-reads" your emails This well-documented practice has been happening for some time. Google's algorithms skim email messages for information about purchases, trips, flights, and more. Solution: Switch email clients The only real solution here is to avoid Google acro

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Are you AI-First? By editor Posted October 13, 2018 In Artificial Intelligence, Digital Strategy, Technology & Design 0 An AI-First company is an enterprise that has transformed to believe and understand the incredible and disruptive potential of Artificial Intelligence. Such an enterprise not only sees the value but can see the destructive impact of being left behind. An AI-First Company understands that it might not know all the answers but realizes that it needs to seek out a path forward with AI or risk being marginalized. Key Characteristics of an AI-First Company A-First companies might not be any different from their previous form but think and act differently. Here are some key characteristics of such companies. Approach to Problems and Planning An AI-First company evolves its approach to problems. AI-First companies realize that determining the existence of a problem and selecting the most consequential problems is a function of data and analytics. An AI-First company invests in building predictive mechanisms that can signal current or upcoming problems including the severity and priority of these problems. Building these predictive mechanisms becomes the first step in determining how and when to prepare for problems or upcoming issues. In addition, these companies leverage news and information that is generated inside and outside the enterprise as it is generated and ensure that their employees and customers have access to the insights embedded in the news and information. Approach to Products and Product Development An AI-First company understands how a prediction or classification could help them deliver a better solution to a problem faced by their customers and how their existing products can be adapted or new products created that change behavior based on the predictions and classifications. Enterprises that understand the power of AI ensure that data scientists come part of the core product ideation and development team with a heavy infl

A lot has been said about agility and the need for enterprises looking to innovate and disrupt to build agility. Agility, at the same time, also gets confused with the process of scrum. Small and large teams get enamored with the idea of scrum and mistake the process with the state of being agile. This is often more detrimental to the enterprise and can often create more process and not enough real agility. What is Agility Agility is the efficiency with which an enterprise executes and delivers on its objectives and goals. Agility is the ability to react to changes in goals, feedback from customers and shifts in strategy. Agility, from the outside, looks like a predictable stream of value delivered by the enterprise that matches and exceeds the needs of the customer. Organizational agility requires agility at multiple levels within the enterprise to drive the insights that can channel and align the efforts of the entire organization by leveraging data and information to make quick and informed decisions. Business Agility Business and customer-facing employees need to achieve "Business Agility". This is the ability of these employees to react to business critical in real time if needed and have access to the latest information at any decision point. Business Agility enables users to reduce the latency or lag between a need in the market or of the customers and when they are able to service the need. Decision Agility Analysts and data scientists creating the insights to drive decisions require "Decision Agility" i.e the ability to easily discover, leverage and use data for analytics and insights through any and multiple tools and channels. Analysts and data scientists need to produce insights that reduce the time and effort required to convert data into information and insights that are required to drive key decisions and actions. Development Agility Application developers and data engineers need the ability to easily generate, collect, access and deli

The quality or the lack thereof can be a huge contributing factor to a fractured and sluggish digital journey where ROI is hard to achieve and results come in short supply. The quality of data has a direct impact on the ability of the enterprise to be aware of relevant events, its reaction time, the decision time and its action time. A clear and concerted effort is required to measure and improve the data quality to drive better decisions and actions. Common Quality Issues The following are the most common quality issues Comprehensiveness Comprehensiveness quality issues refer to key attributes or data points missing from the data collected by the enterprise. This can occur when the data producing systems or the data delivery networks have glitches or malfunction or are incorrectly configured to miss entire rows of data or attributes of the data. Integrity Integrity quality issues refer to the corruption of the values of key attributes to contain unidentifiable or unreadable data. When key attributes are empty or null when they are by design, not allowed to be empty/null or when an attribute contains a value that does not meet the specifications of the type of the attribute for example, a string column contains an integer or a timestamp column contains a string not parse-able into a timestamp. Integrity of data is important before data can be included in the data set to drive analysis, decisions and actions. Sampling Sampling quality issues refer to the inclusion or exclusion of a certain percentage of the records in a data set with the assumption that the remainder records are good, representative sample of the original data set. Bad or inaccurate sampling can lead to a distorted view of reality and that can lead to bad decisions. In addition, sampling itself can make the data set inappropriate for certain types of analysis that require the entire dat set to be utilized for training. Filtering An upstream filtering scheme can end up removing too many or

Lean systems are meant for producing the best products and services with little or no wastage of various kinds of resources. Lean supply chain management purports to create a well designed supply chain management system where all the resources are effectively utilized. The people, processes, technologies and suppliers will be optimized at the beginning of the implementation and it will be an ongoing process. The best of the best features will be implemented and there will be no room for waste. Lean supply chain helps companies deliver high quality products quickly and efficiently. What is expected from lean supply chain management? In fact, lean supply chain management is a new way of thinking to understand the supplier networks. It is required to have cooperation with various suppliers and should be able to balance it with the competition present in the market. There is a great deviation between lean supply chain management and traditional methods. There will be less number of structures in lean model whereas they are more in traditional systems. Most of the lean supply chain management principles are derived from the basic lean principles. How companies will be benefited by the lean implementation? By implementing lean methods in a systematic way, companies will be benefited to the greatest possible extent. The winning business strategy realizes the importance of the relation between the supply chain management and the overall financial status of the company. By streamlining the supply chain management with all the other core aspects of the company, the business will emerge into a profitable enterprise and will be able to deliver cutting edge solutions. Strategy for lean supply chain management In order to implement the lean supply chain management in an effective manner, it is required to build the core infrastructure and should be collaborated internally and should be executed externally as well. The next step would be the selection of the right kind of

According to the Insurance Information Institute, 40% of businesses affected by a natural or human-caused disaster never reopen. When disaster strikes, consumers can only wait so long before moving on to another supplier or service provider if you are not able to get operational in time. Insurance might help to recover some of your loses, but it will do little to replace your customers. More importantly, the safety and well-being of your employees as they cope with disaster is a hefty task in itself to take on. In light of recent events, many organizations are left wondering, are they ready and prepared when disaster strikes? So just what does it mean to be ready? Investing in a preparedness program can make all the difference in the world when disaster strikes and promote an internal belief that your company is ready for disaster when it strikes. This policy should reflect the mission and values of your organization that can be implemented seamlessly throughout the company. Focal points of your preparedness program should detail specific goals including: The overall safety and protection of your employees, visitors, and any other persons that may be at your facility when a disaster occurs. Don't forget to account for persons with disabilities and special needs. Keeping customer service operations functional Protection of assets, both in the physical and electronic form Environmental protection and contamination prevention Companies image and reputation Having a team to help continue the improvement and implementation of your program is vital to its success when needed. Your team should meet regularly to continue the development and focus of the program. Each individual will have their own role and responsibilities when disaster strikes, a proper preparedness program will identify these, insuring an accountable team to help in the recovery process. How do you know what to plan for? It's impossible to predict what the next disaster will be, but accounting f

Tips & Tricks for Efficiently Tracking Lean Projects Recently, we wrote about a couple of problems facing many Lean practitioners. These were problems that hindered their learning process and often caused overconfidence. Amongst the advice to help combat these problems arose the need for detailed tracking and note taking with regards to practice and progress. Being able to accurately track not only the effects but also the process of a Lean project is critical to ongoing success, and is a key to stimulating growth amongst not only the receiving end of your project objectives, but also amongst the employees and Lean practitioners themselves. To start off, let's take a look at the current state of Lean project tracking for many Lean practitioners. A recent LinkedIn discussion posed the very question of project tracking, and sought to understand a few different distinct metrics. In addition to measuring the effectiveness of any program when it comes to completion, tracking can also be about measuring the scope of your projects, so that you can accurately project what the effects will be before you actually reach the stage at which you expect them. Part of this "scope" involves tracking exactly who is involved with your project, who is affected, and how they are progressing in their respective tasks. Where we are now First of all, it's important to evaluate the current methods that are prevalent in Lean record keeping. The LinkedIn discussion starter, self-identified as Ian R., mentions in his opening post that, when he last posed the question about a year ago, the consensus was that most practitioners were simply using excel spreadsheets for their tracking needs. While there's nothing wrong with relying on Excel for the basics, other users were quick to offer up some slicker alternatives, signaling a sharp (and welcomed, in our book) departure from some of the more basic methods. Unsurprisingly, there exist several specialist software applications whose n

"Green Machine" (photo courtesyof Gulfcoastgreenenergy.com) I read a really neat article today on the Gulf Cost Green Energy website, titled "Going Green: The Practical Payoffs of Geothermal Energy," that reported about a guy in Texas named Loy Sneary who has a machine that can convert wasted energy from steam stacks, boilers, and oil wells. According to Mr. Sneary, we waste almost 60% of the energy we use to create heat or electricity. His machine is kind of like a fancy heat pump/steam generator, and just maximizes energy through heat induction between heated air or water and a refrigerant that converts to pressurized steam inside a coil that is then used to generate electricity-all without any emissions. Frankly, I'm not a scientist or even an alternative energy expert, but it sounds reasonably logical. They rigged it up at Texas A&M apparently, and it all just worked out peachy. I'm all for greener alternative forms of energy, and this certainly seems to take the cake for that. I will certainly follow this guy's development of this technology. My only concern is that this article doesn't have too much written up on it-maybe it's hokey, or maybe it's because it's being repressed. Or, it just hasn't had anything written up on it, yet. (conspiracy theories anybody?)

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Success with AI is heavily influenced by the data maturity of an organization i.e. their ability to procure, clean, curate, store and analyze data to power value generating applications. A data-mature enterprise know what data it has, knows what the data means and can ensure that the data is accessible to whoever needs it. Unfortunately, the past few years, driven by the big data hype, have encouraged enterprises to focus on updating their data infrastructure to leverage new big data technologies. With a lot more data now available, enterprises already stuck with massive data storage costs, are being forced to choose between storing data that might eventually be useful for stabilizing, if not reducing their storage costs.

The pertinent use of safety stickers and labels is a must within any industrial work environment. Safety stickers and labels serve many functions, first and foremost, they help to warn or inform employees and visitors of any specific safety concerns. Additionally, they also serve as handy informational reminders from identifying the contents within certain pipes, to showing where recycling may be located, all the way to highlighting the location of a safety eye wash station. Since the need for safety stickers is so important, yet so diverse depending upon the type of business, it may be difficult to locate all the specific stickers and labels needed. One way to easily combat the rising costs of utilizing outside retailers for safety stickers is to simply make your own. Believe it or not, printing your own safety stickers is generally faster, easier, and more cost efficient. Printing Safety Stickers If you've never considered the option of printing your own safety stickers, right now is the time to start. With advances in technology, some label printers are able to efficiently print bright and colorful, commercial grade stickers and labels. One label printing product currently on the market that rivals most others in price, quality, and ease of use is the LabelTac 4 PRO. This printer has proven time and time again to produce high-quality, dependable, colorfast labels that outperform most other safety stickers available on the market, and the best part is they can be printed in-house right from your own office. The LabelTac 4 PRO is capable of creating indoor and outdoor labels that are both weather and fade resistant. Furthermore, the ease of use with the LabelTac 4 PRO is simply impressive to say the least. It is compatible with nearly any Windows-based programs including well-known products such as Microsoft Office, OpenOffice, and Excel. However, even if you don't utilize Windows-based software the LabelTac 4 PRO comes equipped with its own easy-to-use Barte