Today’s Battle for Data – in the Wind and the Cloud

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StrategyDriven Organizational Performance Measures Article | Today’s Battle for Data - in the Wind and the Cloud | Big DataData is the new currency and often the point of strategic control in many industries. Companies are attempting to control data in order to monetize what the data can do for them. Take this example from windmill technology as an illustration:

Windmill technology has dramatically improved over the past few decades. For example, GE has developed blades and rotors that sense the wind direction and adjust a windmill’s tilt/shift in order to optimize its ability to catch the wind. In addition, many windmill “farms” optimize the way they work together since one windmill’s direction and tilt affects the downwind performance of all other windmills. Because a group of windmills operating together is more efficient than individual windmills operating separately, when one windmill fails, the efficiency of the entire farm can be adversely affected.

Industry leaders, including GE and Siemens, have developed their own optimization and monitoring services that use the data coming off the windmills to remotely monitor performance and proactively do repairs to maximize windmill uptime. However, the market for windmills is fragmented with a few large players and a series of smaller players — many of whom are lower-cost manufacturers from Asia and don’t have the scale and/or capabilities to develop and maintain such services.

In response to GE and Siemens’ control of this space, a few ingenious companies are in the process of installing – for free – sensors in both new and existing windmills. These sensors monitor motor vibration and temperature so that they can predict motor failure before it happens. The data are broadcast to the cloud in real time and predictive failure analytics are conducted on the data. Once a motor’s spec goes out of tolerance zones, a team is dispatched to repair the motor before it fails – not only to maximize the “up time” of the windmill, but also to provide peak efficiency for the entire farm.

This enables the smaller players to compete effectively with the larger firms. For example, for smaller Chinese manufacturers trying to compete with GE and Siemens, being able to provide this service is often the difference between making the sale and losing it.

So, how do you make money installing sensors for free? The key is owning exclusive access to the data generated via the sensors and leveraging it by selling higher-margin maintenance contracts back to windmill manufacturers (for newly built windmills) and to farm owners (for retrofitted, existing windmills).

The smaller players are more than willing to allow the sensors to be installed to grant access to the data and pay for higher margin maintenance since they can’t efficiently do this themselves (due to their size and scale). Meanwhile, they gain the ability to compete with the GE and Siemens of the world on services while simultaneously maintaining their cost advantages. In addition, they can eliminate downtime risk via offloading this to its sensor supplier. Therefore, it’s a win-win arrangement for both parties.

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Indeed, this is the modern-day equivalent of the “give away the razor and sell the razor blade” story. Today, the razor equivalent (the sensors) is only of value because of the system’s necessity to interoperate and the ability to monitor it remotely via the cloud and predict failure before it happens. In today’s world, it’s often beneficial to give away the hardware but own the data.

As you design a way to monetize your data-collection system, keep these key principals in mind:

  1. Benefit.The offering needs to provide a clear benefit to both you and your customer.
  2. Incentive.The giving away of the hardware to access the data can’t be for the purpose of simply selling the data to a third party. Rather, access to the data has to enable you to provide better service at a higher price point than rivals. Your unique access to the data makes it so that your customers want to buy from you since, even at a higher price point, you save them money, time and/or resources.
  3. Don’t negotiate the “back end” on the “front end.”In the windmill example, had sensor manufacturers attempted to require maintenance contracts before installing the sensors, they’d likely have received substantial pushback for anything that cost them more. However, once the sensors were in place, the added benefit or performance-based maintenance was clear.

About the Author

StrategyDriven Expert Contributor | Dr. William PutsisDr. William Putsis is a Professor of Marketing, Economics and Business Strategy at the University of North Carolina-Chapel Hill, and a Faculty Fellow for Executive Programs at Yale University. He is also president and CEO of Chestnut Hill Associates, a strategy consulting firm, and founder of the software company, CADEO Economics, which automates his data modeling-based strategy development processes. His new book is The Carrot and the Stick: Leveraging Strategic Control for Growth (Rotman-UTP Publishing, Feb. 3, 2020). Learn more at www.putsis.com or www.chestnuthillconsulting.com.

Business glossary vs. data dictionary

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StrategyDriven Organizational Performance Measures Article |Business glossary |Business glossary vs. data dictionaryWe live in a world that has a prominent and vast scale of complexity to deal with the business and data. We are accumulating all forms of data in every aspect from terminologies to segregating a project. Consider an organization that is building computer processors, this will have a in-sync data base systems and regulations to fill in including the device with its owners captured.

Business Glossary includes unneeded complicate nature. For instance, a startup firm of less 20 employees should not have to gather around the table characterizing Business Glossary terms. Along these lines, if few of IT workers have inquiries regarding what “invoice” explanation. Indeed, they can Skype message to their supervisors or the attorney to discuss about what that business term, among others, mean. The other way toward making a Business Glossary can prompt pedantry about delicacies on characterizing a term, removing time and assets towards being beneficial in a business. Here are a few options in contrast to the Business Glossary vs Data Glossary.

Preliminaries of business glossary

There are many cumulative data bases that has all data of a firm. Data glossary solves or show the way in dealing the complexity nut shell in an organized manner. In this turbulent data world, all the business success rate factors are dependent on each aspect of business process flow.

Values of business glossary

We have governments upheld their laws on some of these business data being a vital role for the future of a country.

Businesses data are being what they are intended, there’s astounding profundity to business glossaries, with the thoughts that is drawn them from such assorted vertices philosophy, semiotics, linguistics, cognitive science, and data science. Let me assure you, however, that while I like to seek out as much as the next information geek, I’ll downplay the powerful stuff for materialness. Not to conclude on how we take up a meeting with valuable information being conveyed and not reproducing the same tech points or flow chart while executing a new project. Business Glossary starts forming its shape from the idea level.

For final business glossary the preliminary step stands with that of the categorization. Within a business glossary, business practices are grouped into categories or clusters according to a set of criteria. Categories are backbone for which they provide the logical structure for the business glossary so that we can manage, make sense of and preliminary find terms within it.

Knowing these connections, it will ensure the categorized information is appropriately managed and kept it updated that are considerable a lot of capacities of an endeavor business glossary.

Preliminaries of data glossary;

We have a few questions that come in right away when you think of data glossary:

  • Where is the information expended within the firm?
  • Is the definition updated, affirmed and accessible or are we surveying with respect to the seasoned terms?
  • Are there synonyms words, abbreviations forms accepted or expressions that portray a similar term?
  • Does everybody utilize similar terms indefinitely?
  • Are there any extraordinary instances for this business term from past to the present time?
  • What are the effects of modification done to the source or to the formats and executing of terms?With these increasing questions thorough bits of knowledge would help a laymen to into the deep comprehension of data, we need perceivability into various components of the Data, for example, utilization, specialized and business prospectus, related policies and rules, just as the rules and duties related with the different data assets.

Values of data glossary

We have come across various cases in a firm when the new task is depending on information where it is shared across divisions and even specialized department. At the point when business lines are reliant on one another’s information, for a strong business glossary becomes basic as every business units has its own needs, responsibilities and useful utilization of data that can be the equivalent or potentially in line with the business and the rights from different clients.

This methodology turns the leverage to use a core arrangement that offers data to meet an objective. Think about the business glossary like the card index in a library. Novel books can cover up a wide range of categorization, for example, life events, history or geology or a thriller novel. They can have explicit characteristics that can assist you with scanning for the book, for example, class, distributer, configuration, writer and production date. Similarly, a business glossary can utilize different arrangements and substance about information to help in the inquiry, accessibility and utilization of the endeavor information. Having this accessible list at a venture level gives a degree of straightforwardness around assisting with maintaining a strategic distance from equivocalness around the information being used.

Diverse business partners characterize basic data glossary in an unexpected way. For instance, take “invoice.” An “invoice,” from the point of view of a buyer, may allude to the document sent that solicitates payment information. In the IT engineering sight an invoice may mean the way toward preparing a bill. In accounting, “invoice” may allude to the record sent to demand payment of a client. An explanation of whether an invoice information component is a physical thing or a procedure becomes necessary when producing invoice reports.

Why Data is Vitally Important to Your Business

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StrategyDriven OPM (organizational performance measures) Article |importance of data|Why Data is Vitally Important to Your BusinessBusinesses have always understood the importance of data and how it relates to their success. From the very beginning, companies knew they needed to understand the market and their customers to make money; the difference was, back then, collecting and accessing data wasn’t easy. That’s not the case today, as technology has helped automate many aspects of data collection, allowing companies of all sizes access to useful information. In fact, nowadays, we have access to so much data, the bigger problem is knowing which data to use and how best to interpret it.

For data to be useful, you have to be able to turn it into actionable insights, and this is where the challenge lies for businesses today. If your company can learn to make better use of its data, it can have huge benefits across your organization.

Decision Making

We make decisions based on the evidence we have available to us. In theory, the more evidence you have access to, the easier it is to make the correct decision, but this is not necessarily the case in real life. When you have an unlimited amount of evidence, it makes it far more challenging to find evidence that will lead you to the right decision. To find the most pertinent evidence to make more informed choices, your company needs people who understand data and know how to apply that data.

You can have all the data in the world, but if you don’t have employees who understand things like SQL join types and can interpret the data, it’s of limited use to your decision-making process.

Problem Solving

Without the evidence that data gives you, it becomes more difficult to spot potential problems and find solutions to fix them. Even the most straightforward processes create data, and this data can help you when problems develop with that process. When you scale this up over an entire business, the data allows you to better understand and solve the issues affecting your organization.

Track Performance

Being able to see how we’re performing against our goals is a crucial element of business success. When correctly interpreted, data can allow you to see how you’re performing in key areas. The insights you gain will enable you to evaluate all areas of your business without any complications. The right people will be able to turn vast amounts of complex data into easy to understand performance metrics that everyone can use.

Improve Processes

This information allows you to make the changes that improve processes across your business. Small changes can have a big effect on the efficiency of your business, and we all have access to the kind of data that can lead to these changes. The challenge is finding the right people to turn that data into actionable insights. The evidence you need to make informed business decisions is there in your data; you just need to be able to find it and use it effectively.

No matter what size your business is, data can play a crucial part in your success.

Stop Drowning In Data And Create An Optimisation Plan

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StrategyDriven Organizational Performance Measures Article |Data Management|Stop Drowning In Data And Create An Optimisation Plan One thing is certain – Big data is big business. As the ways in which we can gather information have expanded almost infinitely, so the data we have stacks up and up. We’ve been promised the earth by understanding our customers better – enhanced profits, more repeat sales, higher average transaction values, loyal brand advocates. And while it’s true that data can deliver all of that, for most businesses, it doesn’t. Because data is a tool like any other, and when it’s misused or not used to its full potential, you’re not likely to see the results. Most businesses collect data without any clear idea of why they are collecting it, and their marketing strategy gets stifled under the sheer amount of available information. Instead of driving the data and mining it to find the relevant parts, it drives them. Learning how to effectively use data is highly individual to each company and their operations and KPIs, but there are some building blocks for good data hygiene and usage that work across all sectors and business types. So, how can you stop drowning in data and start using it to your advantage?

Closing The Feedback Loop

Often we believe that we should be coming up with a lot of colourful looking reports covered in pie charts and bar graphs that we can point to as concrete evidence of macro trends affecting our operations or changes in customer experience. But what do all those colourful reports actually show? Data in and of itself is literally just a bunch of numbers, and all the reporting you like isn’t going to make much of a difference to your bottom line. The most important output is actually the insights that only shrewd analysis can show, and this is the single most important function of the modern marketer. Seeing meanings, patterns and stories is the important part, not the raw data itself. Knowing what all these metrics mean for your business and what action should be taken is the only thing which makes data collection worthwhile.

Make Sure You Measure The Right Thing

The symbiosis between overarching business strategy and analytics can be a tough balance to get right, because both should feed off the other. What you measure should be dependent on what you want to optimise in line with the wider goals you have for your business. But equally, what your goals are should be at least partially dependant on the customer feedback that you amass through your data. Skew the balance too far one way or the other and it’s not going to work in your favour. Setting good metrics for your business is absolutely key to the success you’ll get. Look at things such as which channels drive the most conversions for your business, which landing pages on your website have the lowest conversion rates, what your average order value is in different segments of customers. Underpinning all of these metrics need to be two important things – a great CRM system which can allow you to use these insights to create dynamic marketing campaigns which really respond to individual customer preference and history, and a strict attention to data hygiene and legal practices. Ensure that you’re on the right side of the law when it comes to data collection and storage, and seek out advice from experienced professionals with a track record of legal matter management. The penalties and the damage to your professional reputation can be majorly severe if you get this wrong, so make it a matter of good practice.

Use Segmentation Effectively

Taking action on your data should all be driven by customer segmentation. Not only understanding your customers and their different backgrounds and preferences, but even allocating groups a persona to bring their journey to life and help you see how better to help them. Your knowledge of the goals set out in your business plan should guide which group of customers you look at first, but try to use the data you request to enhance your understanding of each group. This approach allows you to dig a lot deeper and come up with far more creative solutions.

Remember To Add Context

Data is never an island, and if you insist at looking at very narrow ranges of statistics in isolation, the picture that emerges is hopelessly skewed and will never give you an accurate base to work from. A better understanding of context can help you to make much more informed decisions. Make the connection between the figures you’re seeing and what they really mean for your business. Interpreting data badly can be very harmful to your operations and in many cases it would have been better not to collect it at all!

Pull Together Your Optimisation Plan

With the insights you have managed to gather, putting them into some form of actionable plan is the most important part. Six Sigma has a particularly useful concept which can be directly applied to using data insights in this way. The Define Measure Analyse Improve or DMAIC process can be very instrumental in shaping your approach. First, you define the problem that you are trying to solve, known as your hypothesis, set out your relevant stakeholders and the scope of your analysis. Then, you can measure the relevant data fields and use basic analysis to spot any anomalies. The third step is to analyse correlations and patterns within your data set using your visualisation skills to bring it to life. Improvement then corms from using these insights and coming up with a few options to explore. Finally, you control the change by using strategies like multivariate testing and monitoring KPIs to see the impact of what you’re doing. It’s then possible to make responsive adjustments in real time to ensure that your campaigns are fluid and provide a shifting technique to overcome any barriers and generate the best possible return on investment. With a little more careful planning the feeling of being overrun by statistics will be replaced by a focus on only the most relevant metrics to get you to where you need to be.

Designing an AI Strategy for Superhuman Experiences

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StrategyDriven Organizational Performance Measures Article | Designing an AI Strategy for Superhuman Experiences | Artificial Intelligence | Superhuman InnovationThe most difficult question to answer when starting an Artificial Intelligence project is often to determine where to begin. The tendency is to jump straight into the technology without fully defining the problem or examining the market.

Before starting, define what problem needs to be solved and who needs the solution. It’s important to be very specific about your audience because these are the people who will actually purchase or use the product or service. What the end users need can be discovered using a variety of techniques, including market research, surveys and so on. Without defining the problem and the market, it’s likely the ROI will be weak and making sales will be difficult. Often, this is seen as technology for technology’s sake, or doing it just because it can be done. In other words, start with a business problem, an unused data set or survey the new AI techniques, which might identify a problem, a solution and a customer.

To operationalize an AI framework, use the concept of People, Processes, Data and Technology. With People, the concern is with building a team with the right skill set and organization. Processes deal with how the project is developed and the different methodologies available to achieve the goal. With Data, have a data strategy and focus on quality not quantity, as well as accessibility. Finally, Technology provides the software and hardware considerations on which to build the project. This approach can be molded and customized to fit the needs of any project. Just to be clear, this is a blueprint and is not intended as a straitjacket. Use the framework to enable progress, not to restrict your freedom of action.

If an organization is just starting with AI, which many are, change management strategy is very applicable. Change management helps build advocacy and a shared vision within organizations. The thing that many leaders understand is people implement change and that you can’t exclude people from the equation. Plans and processes are necessary but change often fails because the human side is not appropriately factored into the process.

For an AI project to be successful, somebody must ‘own’ it. This doesn’t imply that the project needs to be restrictively managed; rather, one or more senior stakeholders in the business must support the project, its goals and the team. And where the project sits depends on how your company is organized. No matter how a company is organized, the AI team must be embedded within the business and not siloed. If an AI team is isolated from the rest of the business, then their efficiency will be reduced, and they may not consider the needs of end users and stakeholders within the organization.

There also needs to be consideration of how data scientists and AI engineers work together. Are they working as one team or are there multiple teams? Do they work for the same organization? These and other questions must be addressed from the outset. First, you need to define the role of the data scientist. Are they a business or domain expert, statistics expert, programming expert, data technology expert or a visualization and communications expert?

To infuse AI into a company’s culture, communicate throughout the business to increase awareness and acceptance of AI, and build an understanding of the purpose, terms and options available. Your business can also provide educational opportunities to bring members of your organization in all areas of your business up to speed on the concepts. The team can be based out of IT, which would be IT-centric, integrated between data science and IT or a specialized group with team members from throughout the business.

Ultimately, start with the problem and work towards the solution with AI. AI is a profoundly powerful tool to get to that solution, yet there are many things to be considered; including the people who staff the projects and their skills, specialties and experience. However, choosing the right strategic AI framework will guide the project to success.

About the Author

StrategyDriven Expert Contributor | Chris DuffeyChris Duffey is author of Superhuman Innovation: Transforming Businesses with Artificial Intelligence, and the Head of Artificial Intelligence Innovation and Strategy at Adobe. Chris spearheads Adobe’s Creative Cloud strategic development innovation partnerships across the creative enterprise space.

For more information, please visit: https://www.koganpage.com/product/superhuman-innovation-9780749483838