Artificial intelligence (AI) and machine learning already impact our everyday lives. These technologies mostly work so seamlessly with our daily experiences that we barely notice. For instance, machine intelligence powers the digital assistant people use on their phones, movie suggestions on streaming websites, and filters in email. In another generation, it’s possible to imagine that people will consider such revolutions as self-driving cars just as ordinary as their recommended movies on Netflix. Yet, according to CIO Magazine, AI and machine learning are just now making inroads into corporate IT departments.
AI, Machine Learning, and the Changing Roles of Chief Information Officers
In the CIO article cited above, Dave Wright serves as the CIO of Service Now. He said, in the past, his role as Chief Information Officer served to guide, build, and maintain a company’s tech infrastructure. These days, that role has evolved to focus more upon strategizing ways to use technology to benefit his organization. For instance, the CIO may not always choose to expand or even keep their own internal IT infrastructure as much as survey existing technology to see what the business can use to meet its business goals.
Sometimes, this role may involve shrinking the company’s own computing power and partnering more with providers who offer the best solutions. If companies don’t have the resources to develop their own intelligent systems, they can rely upon trusted third parties for solutions.
Facing Internal Resistance to Artificial Intelligence
Wright understands that some members of the IT or other departments may fear changes, specifically the adoption of machine learning and AI. They have concerns that they will detract from their own duties. As was the case during the first days of digital transformation”>digital transformation, these technologies seldom remove jobs but allow people performing those functions to work more productively in a way that supports their organization’s true business goals. While it’s up to CIOs to explore solutions, they also need to communicate the benefits of those solutions to their employees and other executives.
AI’s Penetration into Today’s Businesses
Right now, according to the survey CIO Magazine reported upon, almost 90% of companies do use AI and machine learning in some fashion. However, about 66% of the businesses surveyed are only researching or piloting these new smart technologies. Only about 23% responded that they either used machine intelligence either in several parts of all of their business. Wright believes that most businesses will start by using AI to help interpret and organize information. Only after they feel comfortable with that aspect, will more businesses move to using it to solve problems and later, to anticipate and remediate them.
AI vs. Machine Intelligence
As a note, sometimes people use artificial intelligence and machine learning interchangeably. Artificial intelligence describes computer systems that use their algorithms to mimic human decision-making ability. Machine learning describes a type of artificial intelligence that can use information to adapt its algorithm based upon the information that it receives. In that way, machine learning refers to a kind of artificial intelligence.
Why Cleaning Up Bad Data Matters for Effectively Using AI
As we experience the Information Age, people sometimes refer to data as the new “oil” because of its value. As companies collect more and more information, they increasingly wrestle with problems of data quality. Without proper management, information gets corrupted because its obsolete, redundant, or simply in error. As CMSWire pointed out, new compliance rules like GDPR and the California Consumer Privacy Act also can turn the problem into a regulatory hazard.
Mostly, Wright emphasized that artificial intelligence and machine learning technologies will only work as well as the information that they receive. Luckily, businesses can find an intelligent solution to help them with that task as well. Gartner says that machine-augmented data management will grow common within many organizations. In other words, AI can provide the solution to making certain that it can get the best possible information to base its processing upon.
Why Are CIOs Exploring the Benefits of AI and Machine Intelligence?
Increasingly, high-tech companies are offering smart features to consumers to help them make better and faster choices or to do things more efficiently and safely. It only makes sense that businesses can find plenty of ways to employ this technology to improve their own business processes. Even better, AI tech providers can help level the playing field, so that businesses without the resources to develop their own tech can still access it. These intelligent machines can help companies reduce threats and enjoy more value from the increasingly large amounts of information that they collect.
In the technology solutions space, the Gartner Magic Quadrant release is a very exciting time. As a leading analyst report, the 2019 Gartner Magic Quadrant for Content Services Platforms (CSP) offers established vendors the chance to see where they stack up against their competitors. It gives new entrants exposure. It gives stalwarts in the space some guidance for the future of the market.
But, let’s not kid ourselves. While the Gartner Magic Quadrant gives us, the vendors, key insights, Gartner doesn’t develop it for us. The Magic Quadrant is for you. It’s an indispensable tool for IT leaders and reviewers of CSPs to develop opinions and shortlists of solutions that may help their business. It helps people like you find the features and capabilities in a vendor that are best suited to your digital transformation”>digital transformation goals.
As we mentioned in a previous article, M-Files is proud to be the most Visionary solution in this year’s edition of the Magic Quadrant. It validates our vision and our mission to enable companies and their people to work smarter, work faster and work decisively by unlocking the power of information.
But what many may not realize is that there is a companion report that underpins the Magic Quadrant – the Critical Capabilities report. It acts as the scorecard, quantifying performance in feature sets and use cases. That intel can be especially powerful to companies reviewing CSPs, seeking a well-balanced solution or a mix of certain capabilities.
One key takeaway from this year’s report is the parity between vendor scores. Aside from a few outliers, most scores tend to be pretty close, indicating a mature market and not one with tons of emerging technologies. Indeed, M-Files lives in a competitive landscape where the difference between solutions lies in the minutiae, in the details.
In examining, M-Files scored very well on the critical capabilities. In three of those, M-Files landed at the top of the heap:
Analytics and Reporting
M-Files scored the top spot for analytics and reporting, presumably on the heels of the investment in artificial intelligence and machine learning as an embedded capability of the solution – working to automatically identify and extract valuable data from documents.
An excerpt from Gartner’s description of this capability:
“These features enable users to discover insights about the content and data stored in the CSP. Artificial intelligence in the form of machine learning capabilities has become a major component for the delivery of these capabilities. Analytics and reporting deliver insights to end users. These insights can be extracted from content in the form of text, videos or images. They can also be delivered from the data that is inherent in many CSPs, including metadata and task-/workflow-based tracking. Greater emphasis has been placed in this report on the ability to utilize more advanced analytical capabilities using AI-driven machine learning to provide actionable dashboards.”
Metadata and Classification
M-Files scored the second spot with a 4.7 out of 5 for metadata and classification. We’ve always exclaimed that metadata is the most important part of a CSP, as it remains the force that drives almost every other capability. If an enterprise excels at classifying their information, a whole world of productivity and efficiency becomes reality, as metadata underlies workflows, permissions, searchability, creating relationships between documents and ideas, and so much more.
An excerpt from Gartner’s description of this capability:
“Metadata and classification defines the features that are used to associate metadata with content in a CSP. The ability to create and manage unstructured tags is fundamental here. More advanced features enable users to define structured metadata patterns, thereby enhancing findability and the extraction of insights.”
M-Files took the third spot with a 4.5 out of 5 in content security. Data security is a top priority for businesses. There’s so much more at stake than just fines and penalties for a data breach. The organization’s reputation is at stake, and thus lost business. According to an IBM report, the global average cost of a data breach in the professional services industry is .5 million. The biggest contributor to this cost is lost business. M-Files makes content security a priority, protecting your data from internal and external misuse.
An excerpt from Gartner’s description of this capability:
“Content security capabilities enforce controls that relate directly to the protection of content. Content security is a key capability that is essential for organizations which prioritize privacy and security of the content they store in the CSP.”
Let’s get real. What office worker hasn’t heard of Office 365? Unless you still use a typewriter, you probably spend a great portion of your day in Microsoft Office applications. There are, after all, about 155 million active business users of Office 365, making it the most widely used cloud service by user count.
Regardless, there’s room for more efficiency.
The Main Problem: User Adoption
User adoption is the key to realizing benefits when adding new systems and solutions to your tech stack. And this is one of the key challenges businesses face when digitalizing their workplace and implementing Office 365.
55% of businesses say that the biggest ongoing issue for Office 365 is persuading users to manage and share content in Office 365 and not elsewhere.
Another key issue is how to connect all relevant data, be it structured business data, or unstructured documents.
The Sensitive Document Problem
According to the AIIM 2018 Industry Watch Office 365 Revolution Impact on Governance and Process Automation, 46% of an organization’s sensitive documents are contained in Office documents. The unstructured documents make it hard to manage business critical and sensitive data.
And, to make matters worse, it is estimated that as much as datahub.com/blog/do-not-ignore-structured-data-big-data-analytics”>90% of all data is unstructured, and that unstructured data is stored in at least four different content repositories.
M-Files to the Rescue
According to feedback from our customers, there are five main challenges they face with Office 365 implementations.
Managing content that is stored outside of Office 365 repositories
Finding the best practices to information-management-is-your-secret-weapon-in-data-security/” >manage information
Figuring out where to save information — which Teams site, which channel in Teams
We help solve all those problems. Document management for Teams or other Office 365 tools has never been easier.
Permissions are role-based and easily managed with metadata. Archiving is automated with AI-driven tools. Content outside of Office 365 can still be managed from the familiar Office interface, e.g. Teams. Best practices are enhanced with automated workflows and processes. And M-Files can help you gain access and edit content from different places in one common view.
Bridging the Gap between Structured Business Data and Unstructured Documents
Our unique approach manages information based on what it is rather than where it lives. M-Files connects several business systems and external repositories and allows you to manage information from any repository.
You can easily get a 360-degree view of your business without the pain of migration. Rather, you can gradually migrate the most critical data to M-Files and manage it there. And additionally, allow people to access it through their daily tools.
3 Steps to Increased Efficiency and Convenience
M-Files for Office 365 approaches the challenges of information management on three levels, making office work both more efficient for the business, and more convenient for employees.
Free IT from the pain of migration and integrations. There’s no need for migration as information can be found, accessed, and managed regardless of its origin.
Let people use their familiar daily tools even for document management. Employees can use their daily tools — like Teams or Outlook — to access and manage all documents and data.
Let people focus on value-adding tasks. Employees do not need to waste time looking for information or tagging it as AI can eliminate routine tasks.
It seems we may still be a few years away from feeling the real impact of AI — and a more realistic assessment of the value of AI. Forrester Research VP Srividya Sridharan says: “We believe 2020 will be the year when companies become laser-focused on AI value, leap out of experimentation mode, and ground themselves in reality to accelerate adoption.”
There are several AI trends on the horizon this year for IT leaders — and business leaders, at-large — to follow.
AI will be the sexy, new career path for IT professionals
There will be 133 million new jobs created by AI by 2022, according to the World Economic Forum’s (WEF) 2018 Future of Jobs report. The share of jobs requiring AI skills has grown 4.5 times since 2013. And for those worried about automation and it’s net effect on job displacement, by 2020, AI will eliminate 1.8 million jobs and create 2.3 million, according to VentureHarbor. Hiring for artificial intelligence pros of various titles has increased 74% annually over the last four years, according to LinkedIn.
All of these statistics speak to a new demand in the marketplace for AI and machine learning (ML) professionals — a demand that will spur up-and-coming IT practitioners to focus on AI as a main course of study.
Privacy and governance will come front-and-center in the AI conversation
As far as data governance, 2020 will be about operationalizing AI, making governance a top priority. Forrester states in its 2020 AI predictions report that the problem for AI lies in “sourcing data from a complex portfolio of applications and convincing various data gatekeepers to play along.”
The focus will shift from simply having AI to measuring the impact of AI
Having AI for AI’s sake is passé now. Companies have made the investments and now they will use 2020 to figure out if that investment will bear fruit. “’When you do AI right, it generates value and ROI for the enterprise’ is an excellent premise, however the full potential of AI hasn’t been attained,” says AJ Abdallat, CEO of Beyond Limits. “Many conventional AI systems are merely machine learning, or neural networks, or deep learning. They’re good at handling large sets of data but lack situational awareness or the ability to navigate around missing or incomplete data. They get stuck.”
AI research will reduce speed
No one thinks that the research will come to a screeching halt, but the question is: Can it continue at the steep pace it has been? The demands that AI places on data and processing power may be too much to scale AI objectives, in some use cases — think self-driving cars. We’re still a way off. In an interview with Wired, last month, Facebook AI VP Jerome Pesenti said deep learning research may “hit the wall.”
Pesenti stated, “Clearly the rate of progress is not sustainable. If you look at top experiments, each year the cost is going up 10-fold. Right now, an experiment might be in seven figures, but it’s not going to go to nine or ten figures, it’s not possible, nobody can afford that.”
M-Files has pushed a lot of chips into the middle when it comes to AI in information management. We can verify that AI has risen to the hype, at least in our sector. It will remain a prominent focus of the IT community for years to come, no doubt. IDC figures that by 2025, embedded AI functionality will be incorporated in at least 90% of new enterprise application releases. To temper a bit though, IDC says that comes with a caveat: Truly disruptive AI-led applications will be only about 10% of this total.
AI ain’t going anywhere. But we’re still in a nascent stage, with lots of exciting innovation and healthy disruption to come.
In case you’re new to the topic, metadata refers to the data about the data within documents that you have stored. Some simple examples might include the document title, relevant keywords, and the names of contributors or owners. Initial discussions about metadata tend to revolve around the ways that proper metadata makes searches work faster and better, as we’ve discussed in the past and how metadata adds structure to unstructured information.
At the same time, you need to consider your metadata as more than just input for the kinds of searches that you perform as part of your routine workflow. If you don’t take some effort to plan out your metadata strategy, you could regret leaving out important information that can serve you, your customers, or your partners later.
How Metadata Quality Can Translate Directly to Extra Profits or Losses
Obviously, if searchers can find the information they seek faster and more efficiently, they can work more productively. The oft-quoted IDC statistic says that “the knowledge worker spends about 2.5 hours per day, or roughly 30% of the workday, searching for information.” If that figure is even half right, the consequences of not finding data are huge. In the context of find-ability of information, metadata is like a weathercock pointing users to relevant content — and with M-Files pointing them to that content, regardless of which system that content may reside in.
Your organization should have an easy time translating that benefit into additional profits. Still, improved metadata can even more directly translate into improved profits, better data and security, and lead to money getting paid or spent where it’s supposed to be. Consider a couple of examples that may help you think of some metadata you should plan to include.
An Example of Metadata Issues in the Music Industry
CMSWire addressed this topic by using the example of streaming music with services like iTunes, Pandora and Spotify. Most people who use these services search with obvious tags like musician, song title, or music genre. Still, a contract to pay royalties for each time the music gets played may include plenty of people besides the musicians. Sometimes the sound engineer, producer, and others earn a share. These days, streaming content is not just for amateurs, so the data required to pay royalties has grown more complex and massive since some of these services began.
Apparently, making sure that everybody who deserves a royalty payment is recorded in the song’s metadata has become a real problem in the music industry. Thus, billions of dollars of lost revenue are sunk into a digital black hole because of the different ways that various services record or fail to record this kind of information.
Streaming services can face legal problems and damage to their reputation if they neglect to pay everybody on the contract. Skilled professionals can lose their hard-earned royalties. Pay-per-play services like this must make sure they can track their content reliably.
Saving Metadata for the Future
Most of us probably focus on managing enterprise data for internal use, so nobody’s direct paycheck depends upon it. At the same time, you may be presented with extra tags from certain systems that you don’t need currently to fuel any features of your system. The decision to include or exclude that information can make an impact on your current or future bottom line and, of course, impact profits and perhaps, paychecks.
Certainly, you need to consider features that improve the current user experience and support business functions. You also need to avoid using tags that could have a negative impact. For instance, you may need to take care that personal data from customers doesn’t constitute a privacy violation. However, you should also look ahead to think about some future features that you may need.
An example of metadata you might consider may not just include document authors but also every document editor. Let’s say certain errors have been consistently introduced to certain documents, and you need to find the source of them in order to provide training.
In a potentially worse case, you could need to trace a chain of custody for information in the case of suspected fraud. Even if you don’t need to know everybody who edited a document for your routine business process, you could save yourself a lot of grief if you have it on hand when you do need it.
And let’s not forget the tremendous metadata-creation/” target=”_blank”>impact that artificial intelligence has in automatically suggesting metadata values. Today’s artificial intelligence will help create and add better metadata with less effort. Plus, it will work with all types of files, including text, graphics, audio, and video.
Your Metadata Strategy Can Have a Real Financial Impact on Your Company
You can’t always predict the future; however, you can prepare for it by taking these steps to develop your metadata strategy:
Consider involving various stakeholders when developing your metadata requirements. Certainly, you could invite typical system users and also people who work upstream and downstream of the business area. You might even call in an attorney to discuss potential compliance and other legal areas.
Besides considering routine business practices, try to think about rarer but essential uses of search tags. What kind of information should you keep in case of errors, audits, or even digital mischief?
Finally, consider the direction of growth trends in your business or industry to ensure you capture data when you have it and won’t regret failing to capture it in a couple of months or years. The extra effort you take to plan your metadata strategy today can save your organization money, work, and in some cases, grief.