Profile of Lisa MorganFreelance Writer
News & Commentary Posts: 110
Lisa Morgan is a freelance writer who covers big data and BI for InformationWeek. She has contributed articles, reports, and other types of content to various publications and sites ranging from SD Times to the Economist Intelligent Unit. Frequent areas of coverage include big data, mobility, enterprise software, the cloud, software development, and emerging cultural issues affecting the C-suite.
Articles by Lisa Morgan
Businesses are updating their approaches to data analytics as the competitive landscape changes. We explore the trends, technologies, and vendors leading the way.
Digital transformation requires fundamental changes to the business. Are you ready yet? If you're not thinking about how the following 5 trends affect your business you need to start doing so now.
Faster, better, potentially dangerous. Quantum computers will push way past the boundaries of traditional computers, but to what ends? We explore some of the possibilities.
Biased data can lead to bad decisions. Most business leaders aren't aware of the problem just yet, but they need to be because they're ultimately responsible.
InformationWeek readers know a lot about technology. Many studied computer history as part of their majors and yet, even PhDs would likely flunk a test about African-American contributions to the computer industry. Here's why.
Businesses in virtually all industries are using or experimenting with AI, but do they really understand the ultimate impact AI will have on their businesses? Not completely, according to a new survey by Genpact and FORTUNE Knowledge Group.
Equifax blamed its recent high-profile breach on the Apache Struts Web Framework. As software delivery cycles shrink, developers have to rely on more third-party components, libraries and frameworks. When they do, what are their liabilities and responsibilities?
Drone mapping data can dramatically impact the profitability of certain businesses. Should you include it in your business app? Consider these points.
Analytics ROI often falls short because businesses overlook important parts of the process.
AI and automation are being combined in different ways to complete tasks more efficiently than humans. Every business will be impacted by intelligent automation sooner than may be apparent, so the time to think about it is now.
Insurance agencies struggle to reach new prospects. To adapt to changing markets, they must overcome challenges with data integration, data quality, and systems fragmentation.
If you're having trouble finding and keeping IT personnel, you're not alone. One problem might be that you don't understand what they want, as a new Manpower study suggests.
More hiring managers and HR pros are using social media to make decisions about candidates, but how wise is it? Could the use of social media for hiring be dangerous in its own way?
For PG&E a utility pole inspection app offers much more than a chance to replace paper.
Some organizations race into data-driven transformation. Others want to get everything "right" first. There's an optimal balance between the two.
If you're just getting started with analytics, you can avoid a lot of headaches by learning from the experts. We've tapped a few who have great practical advice.
Enterprises and jobs are changing fast as more tasks are automated with increasing levels of machine intelligence. Automation displaced manufacturing jobs, but it also enabled the creation of new businesses and career opportunities. Will knowledge workers and their employers adapt fast enough?
DevOps has been hyped for a decade, but many still don't understand what it is, let alone what it does. There are many potholes on the road to DevOps. We explain how to avoid some of them.
Change is the only constant, which IT professionals know all too well. However, as technology changes, their departments must too. Some IT departments are in trouble, big trouble if they don't do something fast. We explain a few reasons why.
Are you ready for AI and machine learning? Here's an overview of three use cases to give you a flavor of just what is possible.
More organizations have embraced DevOps to deliver higher quality software faster. Meanwhile, DevOps itself has evolved. We explain where DevOps is today and where it's is headed.
A lot of firms start out as virtual companies, but as they grow, they move into office space. It turns out there are advantages to staying virtual, however. We explain some of them.
Data governance policies are full of holes that can become expensive pitfalls. We explain some of the stumbling blocks.
New technologies and solutions are compelling, so compelling that little thought may go into what will make them successful. Solutions architects can help, but they need help from IT and the business. Thought leaders, including an Interop ITX speaker, share advice.
Agility is the name of the game for today's IT organizations, but keeping up with the rapid pace of change is difficult. We explain what's holding IT back.
There has been plenty of talk about the need for a chief analytics officer or chief data officer. But do you ever wonder what they do for a living?
Users want answers to burning questions, but IT and the data team can't tackle them all at once. Self-service analytics help organizations triage problem-solving by providing many of the insights business users need.
Smart people and the best technology aren't enough to drive a data-driven culture. It's all about people and collaboration, which is easier said than done. If you're serious about affecting change, consider these best practices.
For a company, product, or service to be disruptive, it takes more than technology. Analytics can point you to your opportunities.
GPU-accelerated databases aren't new, and they're not all that popular, but that will likely change over time. Here are some of Kinetica's latest improvements and why that matters to big pharma company GSK.
Analytics is popping up in more functional areas of businesses, outside of IT's control. Like BYOB, the trend is inevitable. How will your organization manage it?
Data is the new oil. Those that figure out how to use it more effectively than their competitors are realizing significant, strategic benefits. But what's so unique about data-first companies? Technology? People? Culture? It turns out, there's more than meets the eye.
Those website recommendations engines sometimes can be just what the consumer needs to make a decision. Then again, sometimes those recommendations are way off base.
Analytics is being embedded in all kinds of software which suggests a major shift is on the horizon. How we think about analytics will change and so will our use of analytics. We explain why.
Analytics are taking on a lead role in all phases of the healthcare system. Those personal fitness devices? Their value is in what they say about us, not the data itself.
Looking for a unicorn? Get in line. Actual data scientists are in high demand, and there's not enough of them to go around. If you want to identify the right talent, consider these tips.
IoT devices are entering the workplace in all shapes and sizes, from workers wearing smartwatches to industrial sensors such as soil monitors. The data pouring in may be so overwhelming it's unclear what should be done with it, why, and what the risks might be. Here are a few ways to navigate the maze.
Applying machine learning and artificial intelligence to your decision-making can help your business stay competitive. But a lot can go wrong along the way. Without the proper checks and balances, machine learning efforts can spiral out of control, exposing your organization to risks. Here are 13 pitfalls to avoid.
Businesses use bots to engage with customers, online and via social media, because they're a cost-effective way to respond instantly to simple queries. As the technology improves, bots are finding their way into more use-cases where human judgment and effort were traditionally required. Are bots right for your business? Here are 10 examples to help you decide.
Even as they aspire to be data driven, organizations are failing to align their vision with execution. Pitfalls lurk everywhere. We've uncovered 10 of the most common culprits.
Regardless of how much data your company has -- and how much your business leaders are asking for -- you're likely missing some hidden gems. Here are 12 examples of what you might be overlooking, and why it matters to IT professionals, and to your business at large.
Companies competing on data need the right skill sets and mindsets in place to succeed over the long term. While more individuals are analyzing data as part of their jobs, their ability to do so varies greatly, even among peers. We've identified 10 key traits of an analytical mind, and explain what to look for in your next hire and what skills to cultivate in your own career.
As organizations look to stay competitive by expanding their use of real-time analytics, implementation becomes a challenge. Finding options to effectively serve your company over the long term is often more difficult than it appears. We've identified 12 common obstacles you'll want to avoid as your company pursues real-time analytics.
Organizations aspiring to become data-driven need to take a close look at their HR practices. If your company's hiring and retention standards aren't keeping up with the times, you may be losing valuable job candidates and employees. To minimize the pitfalls of building a data-savvy workforce, consider these tips.
Somewhere between blind faith and skepticism is the world of prescriptive analytics. Here, machine-generated action items and potential outcomes meet human decision-making. Finding the right balance between algorithms and common sense can be tricky, so consider these tips.
Business is ripe for a bot explosion. The foundational technologies are available, industry behemoths are fanning the fire, users are demanding better experiences, and companies are looking for new ways to optimize their financial performance. Are you ready?
Wearables are finding their way into organizations, whether or not IT departments are prepared to deal with them. As the number of endpoints continues to grow, so does the potential for hacks. These nine pointers will help you prepare your organization to keep ahead of threats.
What does it mean to call your company "data-driven?" Definitions range from simple reporting to viewing data science as a core business strategy. We asked executives from a variety of businesses to help us identify which traits are essential for becoming a truly data-driven company. See what we learned, and tell us how your organization stacks up.
As the latest wave of high-profile breaches shows, all the sensitive information law firms handle makes them attractive cyberattack targets. Here's what can happen and what you should do about it.
Telling a compelling story with your data helps you get your point across effectively. Here are four tips to keep your data from getting lost in translation.
Enterprises are integrating IoT devices into their ecosystems to get data that was not available previously. As with most new data sources, there may be concerns about whether the data is accessible, usable, valuable, and secure. Here are a few things to consider as your enterprise moves toward IoT.
McKinsey & Company estimates that as much as 45% of the tasks currently performed by people can be automated using existing technologies. If you haven't made an effort to understand how artificial intelligence will affect your company, now is the time to start.
Data generation, collection, and analysis are making their way into more types of products and services. The trend is creating new opportunities for innovation, some of which are so impactful, they're causing some companies to revisit their business models. The path to success isn't always obvious, however, so here are a few best practices to keep in mind.
The accelerating pace of global business means that enterprises need more agile data-related systems and practices. Becoming more agile –- and succeeding at it -- isn't always easy given existing technology investments, constant technological evolution, and lingering cultural obstacles. No matter how agile your company is or isn't now, consider these important points.
Most business decision-makers aren't trained to understand data outliers, but they can learn the basics. Executives, managers, and employees without math degrees can ask smarter questions about analyses they're basing crucial judgments on. Here are some things to know.
Companies are using behavioral analytics and sentiment analysis to better understand their customers, but there are still important pieces of insight missing. Few businesses have the ability to engage individuals in a way that is appropriate to the emotions they are feeling in the moment. Emotional analytics can help bridge cognitive gaps between humans and machines.
Organizations are purchasing all kinds of data and analytics solutions, but they still may not be meeting their business goals. If this describes your company, see how insight-driven (vs. data-driven) practices are helping other firms address the problem.
Retailers are getting smarter about delivering better customer experiences across multiple channels. Yet in spite of ongoing technology investments, the promise of relevant, personalized marketing is still falling short. Here are some of the latest tools retailers are using, how they're using them, and what it all means to their businesses and customers.
As more businesses attempt to compete with data, more people within their organizations must be able to gain insight from it. End-user requirements are changing rapidly, often at a faster pace than their employers' ability to deliver sound solutions. Here are a few ways to avoid compromising long-term benefits for short-term gains.
Ambient intelligence promises a world where your car, your home, and your office buildings anticipate your needs. Learn what's happening now, including how people are evaluating the trade-offs between convenience and privacy.
From drug discovery to price optimization, across virtually every industry, more companies are using predictive analytics to increase revenue, reduce costs, and modernize the way they do business. Here are some examples.
More companies are creating data science capabilities to enable competitive advantages. Because data science talent is rare and the demand for such talent is high, organizations often work with outsourced partners to fill important skill gaps. Here are a few reasons to consider outsourcing. What can go right and wrong along the way?
The data science talent shortage has some companies thinking outside the box. Even if your company employs a formidable data science team, you would likely still benefit from third-party ideas or solutions. Data science competitions and other forms of crowdsourcing offer viable means of advancing the art of the possible relatively quickly and cost-effectively. We share some of the possibilities.
The C suite is expanding with more roles dedicated to data and analytics. The Chief Data Officer, or CDO, is one of these roles seen more often in some industries than others. The position may evolve with time. We explain some of the dynamics that cause CDOs to succeed and fail.
After months of legal uncertainty over transatlantic data flows, the European Commission and the US have agreed on a new framework called the EU-US Privacy Shield. But because no text is available yet, there's no way to interpret it. Here's what organizations need to know now.
Even though more organizations are attempting to become data-driven, many of them still aren't able to link data analytics to business outcomes. Some of the challenges are obvious. Others aren't. Here are our tips for avoiding the common pitfalls.
Today's real-time business world requires organizations to make smarter decisions faster. The good news: The technology exists to accelerate decision-making. What's holding you back?
As more organizations embrace the IoT, some may find themselves overwhelmed by the volume of data generated. Edge analytics can reduce the need to store and process it all at a central location. Here are the details.
Data may be a company's most valuable asset, but few are maximizing its economic benefit. Here eight ways that organizations are deriving real, bottom-line value from their data.
Privacy, security, and data ownership issues surrounding Internet of Things devices are creating a host of new legal questions and problems. Here's what's happening now, and what you need to know.
Big data and analytics are set for a big 2016, as more devices and types of software are connected and exchange information. Here are some considerations for businesses as they look to maximize the value of all this data.
Personalization efforts have served as a vanguard for big data at many organizations. But some of these programs still fall short. Here's a closer look at what's going wrong. Does your enterprise fit these profiles?
Enterprise IT organizations face software audits as a matter of doing business with large technology vendors. What's the best approach to dealing with them? Here's a look at what you should and shouldn't do when you get that software licensing audit notice.
Software licensing audits can cause minor annoyance or excruciating pain, depending on their scope and how ready your enterprise is for the disruption and expense. We take a look at trends in software licensing audits and the ways they impact your IT organization and your business.
As unstructured data piles up, semantic technologies help organizations drive business value through a better understanding of the data they have, its value, and the relationships pieces of information have to each other.
Hire or grow from within? Structuring a data team isn't easy because there's no one way to do it right. Here's a look at some pitfalls and best practices.
Technological advances and market forces are driving demand for data scientists, and universities are stepping up to fill the need by expanding their curriculums. Here's a closer look at some of the programs.
The Internet of Things (IoT) has gained momentum. Sensors are now small and cheap enough to embed in all kinds of devices, and more companies are leveraging the vast data generated. Here are some key drivers your company needs to remember as you jump into IoT.
Machine learning is becoming widespread, and organizations are using it in a variety of ways, including improving cybersecurity, enhancing recommendation engines, and optimizing self-driving cars. Here's a look at 11 interesting use cases for this technology.
Modern data-related technologies are changing the way law firms do business. IT executives across all industries will benefit from understanding how these data tools and practices are being applied to legal cases so that you can be ready if you're ever called upon by your organization's legal team. We break it down for you here.
Data scientists and the Wizard of Oz have something in common: Few people really know what they do behind the curtain, which makes it hard to tell good from bad data science. These tips can help you discern the difference.
Cognitive computing, which enabled IBM Watson to win Jeopardy!, is helping professionals get expert assistance and make faster, more intelligent decisions based on highly complex big data.
The amount of data being collected about people, companies, and governments is unprecedented. What can be done with that data is downright frightening. From bedrooms to boardrooms, from Wall Street to Main Street, the ground is shifting in ways that only the most cyber-savvy can anticipate. We reveal the creepy ways to use data now and in the near future.
Contextual analysis, with the help of mobile devices and the Internet of Things, is giving businesses the data they need to reach individuals in relevant and meaningful ways.
The quality of your business decisions is only as good as the quality of the data you use to back them up. Here are some tips to help you determine how reliable your data actually is.
Information governance practices must be updated as laws, technologies, and business models change. Here are seven ways to make sure you're governing your data effectively.
Today's global businesses depend on data, but there are more laws and customs about data privacy than there are countries in the world. The rules are in constant flux. That means it isn't always easy to get reliable data. Here's a look at some of the challenges, region by region.
Data algebra is a new approach for managing, integrating, and searching data faster and more efficiently. Here's why developers and IT departments may want to consider adding it to their toolsets.
There's a lot of gray area when it comes to the ethical collection, use, and analysis of data. Consider these 8 issues organizations should ponder when assessing their data use practices.
Whether naturally occurring or man-made, crises and disasters bring chaos to the people in their path. Learn how governments, nonprofits, and businesses are using big data and analytics to respond in fast, efficient ways.
To drive more value out of your big data, you have to start with the right questions. Here are six strategies for improving the quality of the questions you ask big data.
Data visualizations, used well, can help people make sense of large, complex data. Learn how data visualizations are changing and best practices for making the most of them.
While real-time analytics is getting more affordable, it's still not right for everything. Here are 10 ways to get the most from real time, near real time, and batch use cases.
Whether raising a round of funding or creating shareholder wealth, companies increasingly need a well-articulated and demonstrable data and analytics strategy. Here are some things that can sway an investor's opinion, for good or bad.
Social media data is not just for marketing anymore. Learn how companies are using these insights in novel ways to turn that data into dollars and cents.
A new role is emerging to deal with the ongoing shortage of data scientists. Learn more about these new power users and find out how organizations can cultivate more of them.
Data can prove just about anything. Most organizations want to come to the right decisions, but faulty conclusions and bad outcomes can happen. Here's why.
Flawed data analysis leads to faulty conclusions and bad business outcomes. Beware of these seven types of bias that commonly challenge organizations' ability to make smart decisions.
The pressure's on to use data to outsmart your competitors. Here are six ways companies can use data to imagine and even re-imagine what's possible.