The media buzz that surrounds Big Data, artificial intelligence (AI) and machine learning (ML) has never been higher, so much so that it can overshadow the real applications and actual outcomes companies are working on. But larger than life promises or hype might have an eclipsing affect around the actual, realistic benefits it provides to almost any organization, in a wide variety of industries, that are generating a large volume of data.
The use case benefits are real and it’s time for your company to start harnessing them. But before your organization can receive the value from AL and ML, it must get a thorough understanding of the role it can play at your business, the problems it can solve, and how it can align to your company’s objectives or intended outcome. The problem is that this is where a lot of AI and ML proof of concept (POC) initiatives have stalled and not made a lot of headways. To overcome this, companies need a place to start from. Smaller companies can take a cue card from tech giants such as Google which has started to make bets in solving large scale healthcare issues.
Analytics and Big Dataare everywhere in the pharmaceutical industry providing insights into marketing, sales, clinical trials, claims data, patient demographics, physician engagement and much more. However, before delving into deep analytics, you must master the data, or the insights that you get will at best, be inaccurate, but at worst, cause major issues within the enterprise. Health care lags other industries when it comes to data sharing and interoperability. According to research from Stanford Medicine Health trends report in 2018, the obstacles to data sharing in health care are numerous and include:
- Accessibility: Consumers and health care providers are reluctant to share data with tech companies and non-traditional health care sectors.
- Data Quality: Most data requires thorough cleaning and structure alignment in order to be referenced and shared between systems; there are not enough processes to ensure cleaning gets done.
- Physician Burnout: Doctors are becoming increasingly frustrated with inputting data into EHR systems and find themselves with less time with their patients.
- Privacy and Ethics: Patients are largely uncomfortable with their data being used for research and other purposes; the industry lacks any explicit permissions or anonymization processes.
Ignoring the role of Data Governance is often penny wise but pound foolish.
A lot has changed since the 1950’s when the average age of a publicly-traded company listed on the S&P 500 has gone down from being 60 years old to now less than 20 years old according to research from Credit Suisse investor analysts. Companies that are embracing using new technologies, automation, Big Data, Machine Learning, and innovation are gaining market share on the list, and are disrupting older legacy businesses that have been slower to adapting and transforming to digital changes. In fact 5 of the 6 biggest companies (Apple, Amazon, Alphabet, Microsoft, Facebook) by market cap valuation are data technology businesses. Companies that understand the true value of their data and leverage it with advanced analytics technologies are seeing continued growth. A Forbes contributor Howard Baldwin makes a comparison to prove the point of proving data’s value:
“Why is Facebook currently valued at 415 billion and United Airlines, a company that actually owns things like airplanes and has licenses to lucrative things like airport facilities and transoceanic routes between the U.S. and Asia, among other places, is only worth $24 billion.”
These disruptive innovators, use Big Data solutions as competitive advantages to reduce operational costs, increase revenue, predict behavior, improve cash flows. They weave data into every function of the organization. Data is not only being used to record what has transpired, but it also being used to predict and drive transformative disruptive changes at alarming speeds. And yet how many companies are listing their data as tangible corporate asset on their balance sheet?
When enterprises take on digital transformation initiatives, they may underestimate the power to which digital has been altering industry landscape dynamics, consumer behaviors, economic fundamentals and what it means to stay competitive and relevant with its customers. People often forget how far we have come in a short period of time. The processing power of the latest iPhone is thousands of times faster and stronger than all of NASA’s computers were (combined) during the Apollo moon landing days. These mini computer devices are starting to connect to most of the world’s population, and this all happened since 2007.
Nowadays, iPad tablets and mobile devices have become almost symbiotically interwoven into many facets of our lives. In cities, most people can’t go anywhere without relying on navigational and ridesharing apps such as Google maps or Uber. Planning trips and vacations often come with using apps such as Airbnb, Trip advisor or friend recommendations on things to do, see and eat on Facebook. And even on vacation as long as there is a Wi-Fi connection, you can use your phone to check your email, listen to music, post and share experiences to Instagram, check your investment portfolio, automate and monitor your home, facetime your relatives, search the internet for movie or gift recommendations.
My job as Big Data Strategy professor and tech entrepreneur entails coaching and consulting with CIOs, CDOs, CTOs of enterprise organizations. But, when I look at these magical little smart phone devices, and all of the digital changes and technology innovations it can and will enable going forward, it usually makes me want to have a ‘come to Jesus moment’ with them. Some executives might have the belief that having a few technology initiatives in the works
such as AI pilot programs, automation and the integration of physical supply chains with digital technology is enough, well it’s not.
When you hear the word “data scientist,” what does that term mean to you?
Is it the “sexiest job” of the 21st century as the Harvard Business Review suggested? Does it describe a really smart person with advanced degrees in computer science, applied math, statistics, economics? Someone who analyzes and extracts business value from big data?
A data scientist can be all of these things and more. This type of professional looks for patterns and trends in large sets of data, using a variety of tools, techniques and critical thinking to arrive at practical solutions to real-life data-centric problems.
According to Hugo Bowne Andersen in HBR, “Data scientists use online experiments, among other methods, to achieve sustainable growth. They also clean, prepare, validate structured and unstructured data to build machine learning pipelines, and personalized data products to better understand their business and customers and to make better decisions.”
Now, even if you didn’t go to school in advanced analytics and data science, understanding the thought process data scientists go through might help your early-stage startup understand what it is exactly these professionals do:
Most enterprise companies interact with, operate on, and leverage data across a vast array of business departments. Data is generated by everything from web apps to cameras to heart rate monitors to Internet of things (IoT) sensors, which is empowering richer insights into human and “things” behaviors. Companies that want to make the transition into being a ‘data driven organization’ may entail coordinating operational business decisions to a systematic interpretation of information by deploying Advanced Analytics. With the goal of becoming adigital businessthat uses analytic insights to capitalize and launch new business opportunities. Now you might think that it is quite obvious that companies would understand the importance of using data. But, you would be surprised how many organizations do not fully align their Data Strategy with their business objectives.
How is it that some companies such as Netflix can spot technology trends and shifting consumer habits to make organizational pivots from a being DVD rental service to an online streaming subscription to finally becoming an award winning “original show” content producing internet TV juggernaut? And others such as Borders Books or Kodak couldn’t foresee market changes and failed to adapt and innovate to stay relevant to consumers.
I have previously taught a course called “Leading with Innovation: Align your Data Strategy with Business Strategy” on this exact topic. In this article, I plan to cover a few key elements that integrate data and innovation into Business Strategy.
You can’t seem to get away from seeing tech headlines about how many companies want to adopt or use AI for their Business Intelligence applications and technologies. From a Forbes article titled ‘“Preparing Your Business For The Artificial Intelligence Revolution” to a Martech advisor one called “How to Reshape Your Business Strategy Around AI.”
But how does a company plan and go about implementing Artificial Intelligence technologies into their business? The first step is to figure out the reason for wanting to use AI in the first place. Digital organizations are beginning to experiment and incorporate different types of AI applications such as Artificial Neural Networks, or Machine Learning into their processes and products for tasks such as image recognition, NLP, sales forecasting, autonomous driving, robotics, patient data processing, fraud detection, personalized recommendations, and chatbots. Forbes contributor Terrence Mills, wrote an article called “Machine Learning vs Artificial Intelligence: how they are different” which provides good overview summary of the what each does.
MIT Sloan review discussed why AI implementation ischallenging. The path to implementing AI can be a costly and a long-term investment. It is not as easy as moving your business into the cloud or implementing a new Big Data Analytics tool. One of the main reasons that implementing AI can becost prohibitiveand complicated is due to the fact that every use case or application of it needs different algorithms and technology tools for it all to work within the system or processes to make it successful. There is no one size fits all AI algorithm.
If your company is one of the 84 percent that believe that investing AI will lead to competitive advantages and you are looking to explore your options for adopting cognitive technologies such as AI into your business, then here are a few tips:
Asha Saxena talks with Adam Kiefer, Director of TEAM VR (Training Enhancement and Analysis of Movement Virtual Reality) Laboratory at Cincinnati Children’s Hospital Medical Center, about the emerging use of Artificial Intelligence in sports medicine.
Deep learning is rapidly becoming an indispensable data tool in the world of sports. Deep Learning, a subset of AI focused on recognition and reinforcement learning, is being used in athletics to gather valuable information that can be used by medical experts to better understand sport-related injuries and develop new therapies. Deep Learning applications are being trained on large sets of data and then used to derive strategic insights into areas such as player capability, team tactics and injury management.
AI can be used in sports medicine for treating injuries, but more importantly it is an emerging technology in the effort to predict and prevent injuries altogether. Learn more on today’s podcast. Continue reading
Asha Saxena explores data and analytics governance with Isaac Wagner, Director of the Strategic Analytics group at Memorial Sloan Kettering Cancer Center in New York, a state-of-the-art cancer treatment facility using cutting-edge innovations to treat all kinds of cancer.
In this episode, Isaac Wagner discusses the importance of having a solid governance structure, the difference between data groups and analytics groups, and how data can be utilized to inform decision-making and problem-solving within a business.
Governance is all about behavior. Workforce skills, technology, and organizational models are aligning to ensure that the data available to a business is factual and consistent. Learn more on today’s podcast. Continue reading