Archive for Brian Berry

Three Steps to High Quality Master Data

Data quality is critical to business, because poor business data leads to poor operations and poor management decisions. For any business to succeed, especially now in this digital-first era, data is “the air your business needs to breathe”.  If leadership at your organization is starting to consider what digital transformation means to your business or industry – and how your business needs to evolve to thrive in these changing times, they will likely assess the current business and technology state. One of the most common outcomes management may observe is that the business systems are “outdated” and “need to be replaced”. As a result, many businesses resolve to replace legacy systems with modern business systems as part of their digital transformation strategy.

Digital Transformation Starts with Data

More than likely, those legacy systems did a terrible job with your business data. They often permitted numerous, incomplete master data records to be entered into the system. Now, you have customer records which aren’t really customers. The “Bill To’s” are “Sold-To’s”, the “Sold-To’s” are “Ship-To’s”, and the data won’t tell you which is which. You might even have international customers with all of their pertinent information in the NOTES section. Each system which shares customer master data with other systems contains just a small piece of the customer, not the complete record.

This may have been the way things were “always done” or departments made due with the systems available, but now it’s a much larger problem, because in order to transform itself, a business must leverage its data assets. It’s a significant problem when you consider all the data your legacy systems maintain. Parts, assets, locations, vendors, material, GL accounts: each suffer from different, slightly nuanced data quality problems. Now it hits you: your legacy systems have resulted in legacy data.  And as the old saying goes – “garbage in, garbage out.” In order to modernize your systems, you must first get a handle on data and your data practices.

Data Quality Processes

The data modernization process should begin with Master Data Management (MDM), because MDM can be an effective data quality improvement tool to launch your business’ Digital Transformation journey. Here’s how a data quality process works in MDM.

Data Validation – Master Data Management systems provide the ability to define data quality rules for the master data. You’ll want these rules to be robust — checking for completeness and accuracy. Once defined and applied, these rules highlight the gaps you have in your source data and anticipate problems which will present themselves when that master data is loaded into your shiny new modern business applications.

Data Standardization – Master Data thrives in a standardized world. Whether it is address standardization, ISO standardization, UPC standardization, DUNS standardization, standards assist greatly with the final step in the process.

Matching and Survivorship – If you have master data residing in more than one system, then your data quality process must consider the creation of a “golden record”. The golden record is the best, single representation of the master data, and it must be arrived at by matching similar records from heterogeneous systems and grouping them into clusters. Once these clusters are formed, a golden record emerges which contains the “survivors” from the source data. For example, the data from a CRM system may be the most authoritative source for location information, because service personnel are working in CRM regularly, but the AR system may have the best DUNS credit rating information.

Modernize Your Data and Modernize Your Business

BB Art

These three data quality processes result in a radical transformation in the quality of master data, laying the foundation for critical steps which follow. Whether or not your digital transformation involves system modernization, your journey requires clean, usable data. Digital transformation can improve your ability to engage with customers, but only if you have a complete view of who your customers are. Digital transformation can empower your employees, but only if your employees have accurate information about the core assets of the business. Digital transformation can help optimize operations, but only if management has can make informed data driven decisions. Finally, digital transformation can drive product innovation, but only if you know what your products can and cannot currently do.

Berry_Brian-240About Brian: Brian Berry leads the Microsoft Business Intelligence and Data Analytics practice at BlumShapiro. He has over 15 years of experience with information technology (IT), software design and consulting. Brian specializes in identifying business intelligence (BI) and data management solutions for upper mid-market manufacturing, distribution and retail firms in New England. He focuses on technologies which drive value in analytics: data integration, self-service BI, cloud computing and predictive analytics. 

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Intelligent Apps Are Friendly Apps

Whether you are a human or a computer, it pays to be friendly. When you buy something, are you more likely to buy from friendly or unfriendly salespeople? I like to spend time with people, but only if they are friendly. I am more apt to be generous with people who are friendly and am more easily persuaded by friendly people.

With technology, I love to interact with friendly software, or should I say “intelligent apps”. What makes for an intelligent app? Well, they are apps which exhibit a kind of machine intelligence which we associate with human intelligence. Not “super computers,” but computers and software which exhibit the same qualities I enjoy in friendly people. Let me get a little more specific:

  • People whom I have met before usually recognize the sound of my voice. Those who listen to what I say are ones which I admit to my inner circle. My friends may disagree with what I say, but I know that they listen and understand me.
  • People whom I have just met make some guesses about my mood and interact with me accordingly. My friends recognize my mood pretty quickly when they converse with me. My close friends always seem to respond to me in ways that are intended to bring me back to a positive frame of mind.
  • Most humans I come across recognize that, when I get to the “heart of the matter,” I am not performing surgery, or dealing with organs in any way. Only a literal minded person, or a super-computer, would come to that conclusion.
  • Finally, I often come across humans who do a great job of sharing knowledge with me. When I ask questions, they provide me with a lot of great information. I enjoy spending time with people who are knowledgeable, yet humble, and try to maintain contact with them professionally.

Of course, computers and software have historically not done any of these things well! It’s no wonder many people may find them infuriating. Our computers and software just haven’t conformed to our perceptions of intelligence – therefore, we don’t perceive them as friendly. But, longstanding ideas about what artificial intelligence (AI) looks like have inspired what are called “Cloud-Based Cognitive Services.” In other words, scientists and engineers have figured out that cloud computing, big data and data sciences have enabled the technologies needed to deliver AI.

Meet Your New Best Friends

I think the thing which is so attractive about “intelligent apps” is that I perceive them as being friendly.  Take Windows Hello, the facial recognition software in Windows 10 which recognizes your face as your login. I much prefer logging onto my Surface Pro 4 at home (which has Windows Hello) than my work laptop (which does not). My face never expires, does not need to be reset, and doesn’t need to be remembered! This is just a fabulous experience; it’s almost as though my tablet “knows me.”

Here is another example of intelligence which makes life easier- natural language processing in Power BI. Before natural language processing, I had to apply filters to my data, click around to find the thing I was looking for and format the graphs and charts on my report. With Power BI, I can simply type “Show Me Last Year’s Sales by Territory” and the data appears. This is simply one example. Power BI dashboard authors do not even have to have created a report in order for this intelligent app to suggest it as a possible solution. When paired with the voice recognition capabilities of Cortana, it may seem that you have a digital assistant with limitless access to the dashboard, reports and data you need to run your business.

Cloud-Based Cognitive Services

Today’s modern applications are intelligent apps, and the hallmark of an intelligent app is human-like artificial intelligence. Most application developers do not have access to the AI algorithms needed to be truly effective. However, the giants of cloud computing have made these capabilities easy to acquire and integrate into your next product, service or business systems.

Microsoft Cognitive Services are a set of Cloud Application Programming Interface’s (API’s) which application developers can embed into their modern apps to make them intelligent. There are API’s for visual recognition, speech recognition, text analytics, recommendations and much more. Perhaps you want to create an app which recognizes a face, or a user’s voice. Perhaps you want to create an app which interacts with users differently based upon the user’s perceived mood. Perhaps you want to make recommendations to customers on your website. It’s all possible, and in fact, a lot easier than you might imagine.

BlumShapiro Consulting is a Microsoft Advanced Analytics partner, with expertise in building modern intelligent apps. And we are extremely friendly.

Berry_Brian-240About Brian: Brian Berry leads the Microsoft Business Intelligence and Data Analytics practice at BlumShapiro. He has over 15 years of experience with information technology (IT), software design and consulting. Brian specializes in identifying business intelligence (BI) and data management solutions for upper mid-market manufacturing, distribution and retail firms in New England. He focuses on technologies which drive value in analytics: data integration, self-service BI, cloud computing and predictive analytics. 

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Applied Machine Learning: Optimizing Patient Care in Hospitals, Profitably

Why are so many industries exploring Machine Learning as a means of delivering innovation and value?  In my view, the technology speaks to a primitive urge – machine learning is like having a crystal ball, telling you what will happen next.  For a business, it can convey information about a customer before they introduce themselves.  On a personal level, when I consider what I would like to have information about in advance, the first thing that springs to mind is obvious: my health.  Am I about to get sick?  How can I improve my wellness and overall health?  If you are wearing a Fit-Bit right now, then you probably agree with me.

In my last blog, I shared some real-world examples for how the Hospitality Industry applies Machine Learning.  What about Health Care and Hospitals?  While hospitals have similar challenges, in that they accommodate guests who stay overnight, the objectives in health care are quite different, and changing rapidly.   The Affordable Care Act is driving new business models, incentivizing outcome based reimbursement as opposed to volume based reimbursement.  Unlike hotels, today’s hospitals are interested in ensuring their guests do not have to return, at least not in the short term.   They also need to manage costs in a way they have not been incentivized to do in the past.  Hospitals across the country are considering how predictive analytics can have a  meaningful impact on operations, leading to improved patient health and improving the bottom line.

The cost savings opportunity for health care providers is startling. Here are just three examples:

Reducing Hospital Readmissions – in 2014, Medicare fined 2,610 hospitals $428 million for having high hospital readmission rates. Leaving actual fines aside, industry analysts estimate that the overall cost of preventable readmissions approaches $25 Billion annually. As a result, hospital systems all over the nation are mobilizing to intervene, using ML to identify risk factors which are highly predictive of readmission. Carolinas Healthcare System, partnering with Microsoft, did just that. Using data from 200,000 patient-discharge records, they created a predictive model deliver customized discharge planning, saving the hospital system hundreds of thousands of dollars annually. Read the article in Healthcare IT News.

Clinical Variation Management – Mercy Hospital is partnering with Ayasdi to find the optimal care path for common surgical procedures. Using knee replacement as an example, the Clinical Variation Management software helps hospital administrators find clusters of patient outcomes, then enables the exploration of those clusters in order to correlate a metric (i.e. Length of Stay) with a certain regiment or activity. Watch this video to learn how Mercy Hospital saved $50 million by applying Machine Learning to an extremely common procedure.

Improving Population Health – Dartmouth Hitchcock, a healthcare network affiliated with Dartmouth University, is piloting a remote monitoring system for patients requiring chronic care. 6,000+ patients are permitting the hospital to collect biometric data (i.e. blood pressure, temperature, etc.) in order that nurses and health coaches can monitor their vital signs, and machines can predict good days and bad days. Quite the opposite of hospitality: Dartmouth Hitchcock is trying to keep the guests from needing to checking in! Read more about the Case Study from Microsoft.

Are machines taking over for physicians? No. The Patient – Physician relationship remains (and I think will always remain) central to the delivery of personal health care. However, it seems clear that ACA is providing significant rewards to health care providers who manage population risk better. Machines can help here: through data, machine learning can find risk “hiding in the data”.

Contact Blum Shapiro Consulting to learn more about how Azure Machine Learning can curtail hospital readmissions, identify variations in common clinical procedures and improve patient population health.

Applied Machine Learning – Hospitality and Hotel Management

Machine Learning, or Predictive Analytics, is in common use in the travel and hospitality industry.  Online travel sites such as Expedia, Priceline and Trip Advisor have been putting data to use for over a decade, and they are very good at selling us stuff with Machine Learning.  They start by figuring out what someone will pay (relative to other customers) for a travel service, classifying preferences for all of the travel services they have available to sell, and recommending the next thing a website visitor need.  Flying to Vegas you say?  It’s very likely a hotel or car rental is in your future.  But which hotel offers will keep you on their site, buying from Expedia and not Expedia’s competitors?  Do you expect a premium hotel experience?  Do you need a Fiat or a GMC Acadia?  Are you a fan of Blue Man Group, Celine Dion or Penn and Teller?

And they usually get it right! They are using what is called a Recommendation Engine in order to guide customers through a series of buying decisions, based upon patterns and preferences which are similar to other customers.  Underneath, it’s just math, but the impact is undeniable.  It enables companies to predict what their customers want, and then (this is the critical piece) anticipate and deliver a response to that want, at a time when a customer truly needs it and is willing to pay for it.  Right thing, right place, right time.

Recommender is one of four core Machine Learning problems.  These four problems represent what ML can solve.  Now let’s apply Machine Learning to another business model within the Hospitality vertical: Hotel Management.  Is machine learning relevant to hotels?   If an algorithm can anticipate a guest’s need, what can hotel staff do with that information? The answer is: create a customer for life.

One common misconception about Machine Learning is that, in order to produce any useful insights, you need tons and tons of data (or, Big Data).  Property Management Systems (PMS) used by hotels and hotel groups are proving this wrong: they are using ML on a small scale to manage revenues, improve profitability and delight their customers.  Here are three examples of how hotel and property management groups are using Machine Learning to manage revenue today.

Market Segmentation – Customer segmentation is not a new thing.    We understand the difference between family vacationers and business travelers – those are big segments. But Machine Learning goes further, helping companies discover segments they may not realize existed. Which customers want to be near the pool, and which ones need three morning papers before they can even get dressed in the morning? Armed with this knowledge, hotels understand what matters to guests, at the individual level, enabling them to anticipate their needs immediately. Even more, hotels can understand key characteristics of their most profitable customers, and recognize the next one when they login to the online reservation site.

Demand Forecasting – what kinds of services will be in demand throughout the year? Can a hotel optimize room offerings to suit changing demand patterns, and perform renovations or scheduled maintenance in a way that has no net impact on profitability? While hotels may have limited ability to grow or shrink their inventory of rooms, management can optimize resource needs in order to respond to predicted demand levels.

Price Optimization – airlines understand very well that different customers are happy to pay different prices for the same airplane seat.  The same is true for hotel rooms.  Often, profitable customers are ones who are least price sensitive, but some are quite price sensitive – choosing to spend their money on other hotel services offered.

Machine Learning may seem intimidating, especially if you are assuming you’ll need Big Data and massive computing power in order to yield an investment.  Actually, the key ingredient is knowledge of your industry, and what you, as a services provider, can offer customers.  Most hotels and hotel groups have enough core guest information to begin deriving valuable insights and begin competing for customers in the new world of data.

Contact Blum Shapiro Consulting to learn more about how Azure Machine Learning can deliver these kind of predictive capabilities to your business, leading to sustained customer loyalty and profitable business outcomes.