by Elizabeth Hines | Apr 8, 2015 | Blog, Leadership, Strategy
Mergers and acquisitions are increasingly popular strategies toward growth; however, 40% to 80% of mergers fail to meet objectives. M&A is complicated, and goes beyond simply “the process of buying a company.” At its heart it is a strategic selection of competencies that fill a void in a company’s offering, geography, technology, or industry area of focus. It’s wise to think about whether the time, money, and energy are ultimately going to pay off, literally and figuratively.
There are some critical things to consider before courting a merger or acquisition. Be a leader by asking the tougher questions internally rather than focusing your team on an outside “target”:
- Is there clarity around why a merger or acquisition is being considered? Will your organization reap strategic benefits or is this potential change only going to bring bonuses to the executives?
- Can your reorganization be better served by forming a strategic alliance instead? In this way, you get what you need without other non-strategic pieces that cloud merger and acquisition return on investment.
- Is there a licensing strategy that would work better than an acquisition strategy? Again, you can reach a beneficial goal without the expensive and time-consuming complications of a merger or acquisition.
- Are there other ways to access the marketplace, the capabilities, or the geography that you desire from the acquisition target?
After examining these questions, if the strategic decision points to a merger or acquisition, then strong leadership is critical. In the Deloitte report, The Leadership Premium, a survey of 400 stock market analysts ranked “senior leadership team effectiveness” as second only after “financial results” as the top criteria for judging company success. A detailed review of 94 different mergers revealed that leaders who oversaw a successful merger could:
- motivate others
- influence others
- build relationships
- develop others
- act with integrity
- show adaptability
- focus on customer needs
If acquiring leaders haven’t properly engaged with the target company before, during, and after an acquisition or merger, the likelihood of success declines. Confidence among employees of the acquiring and target companies can waiver throughout the acquisition process, and the same can happen during a merger. More than ever people will look to leadership for answers and guidance. Employees ask themselves: will I lose my job? Will I need to relocate? Will my position change? Will the workplace culture change? These answers will need to come, and for many employees, the earlier the better. A study found that “two of the top five most common reasons for M&A failure were down to management. These reasons were: poorly managed integration of people and culture (60%) and poorly managed integration of systems and processes (54%).”
From target identification to post-deal integration, leaders must become more involved with the steps necessary to make a merger or acquisition successful. Without such leaders, and their willingness to engage and guide, there could be no deal, or a very sour one.
by Elizabeth Hines | Apr 8, 2015 | Blog, Leadership, Strategy
Mergers and acquisitions are increasingly popular strategies toward growth; however, 40% to 80% of mergers fail to meet objectives. M&A is complicated, and goes beyond simply “the process of buying a company.” At its heart it is a strategic selection of competencies that fill a void in a company’s offering, geography, technology, or industry area of focus. It’s wise to think about whether the time, money, and energy are ultimately going to pay off, literally and figuratively.
There are some critical things to consider before courting a merger or acquisition. Be a leader by asking the tougher questions internally rather than focusing your team on an outside “target”:
- Is there clarity around why a merger or acquisition is being considered? Will your organization reap strategic benefits or is this potential change only going to bring bonuses to the executives?
- Can your reorganization be better served by forming a strategic alliance instead? In this way, you get what you need without other non-strategic pieces that cloud merger and acquisition return on investment.
- Is there a licensing strategy that would work better than an acquisition strategy? Again, you can reach a beneficial goal without the expensive and time-consuming complications of a merger or acquisition.
- Are there other ways to access the marketplace, the capabilities, or the geography that you desire from the acquisition target?
After examining these questions, if the strategic decision points to a merger or acquisition, then strong leadership is critical. In the Deloitte report, The Leadership Premium, a survey of 400 stock market analysts ranked “senior leadership team effectiveness” as second only after “financial results” as the top criteria for judging company success. A detailed review of 94 different mergers revealed that leaders who oversaw a successful merger could:
- motivate others
- influence others
- build relationships
- develop others
- act with integrity
- show adaptability
- focus on customer needs
If acquiring leaders haven’t properly engaged with the target company before, during, and after an acquisition or merger, the likelihood of success declines. Confidence among employees of the acquiring and target companies can waiver throughout the acquisition process, and the same can happen during a merger. More than ever people will look to leadership for answers and guidance. Employees ask themselves: will I lose my job? Will I need to relocate? Will my position change? Will the workplace culture change? These answers will need to come, and for many employees, the earlier the better. A study found that “two of the top five most common reasons for M&A failure were down to management. These reasons were: poorly managed integration of people and culture (60%) and poorly managed integration of systems and processes (54%).”
From target identification to post-deal integration, leaders must become more involved with the steps necessary to make a merger or acquisition successful. Without such leaders, and their willingness to engage and guide, there could be no deal, or a very sour one.
by Elizabeth Hines | Apr 6, 2015 | Big Data, Blog, Data/Analytics
Concurrent with the extraordinary rise of the Internet of Things (IoT), predictive analytics are gaining in popularity. With an increasing number of companies learning to master the precursors to developing predictive models — namely, connecting, monitoring, and analyzing — we can safely assume the art of gleaning business intelligence from foresight will continue to grow rapidly.
Indeed, Gartner analysts put forward that “few technology areas will have greater potential to improve the financial performance and position of a commercial global enterprise than predictive analytics.” And executives seem eager to jump on the bandwagon; in a survey of executives conducted by Accenture a full 88% indicated big data analytics is a top priority for their company. Amid the promises of predictive analytics, however, we also find a number of pitfalls. Some experts caution there are situations when predictive analytics techniques can prove inadequate, if not useless.
Here, we dissect three problems most commonly encountered by companies employing predictive analytics.
Past as a poor predictor of the future
The concept of predictive analytics is predicated on the assumption that future behavior can be more or less determined by examining past behaviors; that is, predictive analytics works well in a stable environment in which the future of the business is likely to resemble its past and present. But Harvard Business School professor Clayton Christensen points out that in the event of a major disruption, the past will do a poor job of foreshadowing future events. As an example, he cites the advent of PCs and commodity servers, arguing computer vendors who specialized in minicomputers in the 1980s couldn’t possibly have predicted their sales impact, since they were innovations and there was no data to analyze.
Interpreting bias
Consider also the bias in favor of a positive result when interpreting data for predictive purposes; it is one of the most common errors in predictive analytics projects. Speaking at the 2014 Predictive Analytics World conference in Boston, John Elder, president of consulting firm Elder Research, Inc., made a good point when he noted that people “‘often look for data to justify our decisions,’ when it should be the other way around.”
Collecting and analyzing unhelpful or superfluous data
Failure to tie data efforts to operational processes can lead to an unnecessary drain of staff resources. Mining big data will do little good if the insights are not directly tied to functional procedures. More companies than we probably realize are wasting precious time and manpower on big data projects that are not adequately understood, producing trivia rather than actionable business intelligence.
To overcome these common pitfalls of predictive analytics, spend some time reviewing the sources of your data and the basic assumptions on which your predictive analytics projects are based. Because the major principle of predictive analytics is that the past behavior can forecast future behavior, keep your ear to the ground for growing industry trends or any other factors that might influence consumer behavior. Plan to revisit the source of your data frequently to determine if the sample set is representative of your future set and should continue to be used. Most importantly, regularly evaluate how your predictive analysis relates and contributes to your company’s overall goals and objectives.
What else do you think organizations can do to ward off the snags of predictive data analysis and use foresight more effectively?
by Elizabeth Hines | Apr 6, 2015 | Big Data, Blog, Data/Analytics
Concurrent with the extraordinary rise of the Internet of Things (IoT), predictive analytics are gaining in popularity. With an increasing number of companies learning to master the precursors to developing predictive models — namely, connecting, monitoring, and analyzing — we can safely assume the art of gleaning business intelligence from foresight will continue to grow rapidly.
Indeed, Gartner analysts put forward that “few technology areas will have greater potential to improve the financial performance and position of a commercial global enterprise than predictive analytics.” And executives seem eager to jump on the bandwagon; in a survey of executives conducted by Accenture a full 88% indicated big data analytics is a top priority for their company. Amid the promises of predictive analytics, however, we also find a number of pitfalls. Some experts caution there are situations when predictive analytics techniques can prove inadequate, if not useless.
Here, we dissect three problems most commonly encountered by companies employing predictive analytics.
Past as a poor predictor of the future
The concept of predictive analytics is predicated on the assumption that future behavior can be more or less determined by examining past behaviors; that is, predictive analytics works well in a stable environment in which the future of the business is likely to resemble its past and present. But Harvard Business School professor Clayton Christensen points out that in the event of a major disruption, the past will do a poor job of foreshadowing future events. As an example, he cites the advent of PCs and commodity servers, arguing computer vendors who specialized in minicomputers in the 1980s couldn’t possibly have predicted their sales impact, since they were innovations and there was no data to analyze.
Interpreting bias
Consider also the bias in favor of a positive result when interpreting data for predictive purposes; it is one of the most common errors in predictive analytics projects. Speaking at the 2014 Predictive Analytics World conference in Boston, John Elder, president of consulting firm Elder Research, Inc., made a good point when he noted that people “‘often look for data to justify our decisions,’ when it should be the other way around.”
Collecting and analyzing unhelpful or superfluous data
Failure to tie data efforts to operational processes can lead to an unnecessary drain of staff resources. Mining big data will do little good if the insights are not directly tied to functional procedures. More companies than we probably realize are wasting precious time and manpower on big data projects that are not adequately understood, producing trivia rather than actionable business intelligence.
To overcome these common pitfalls of predictive analytics, spend some time reviewing the sources of your data and the basic assumptions on which your predictive analytics projects are based. Because the major principle of predictive analytics is that the past behavior can forecast future behavior, keep your ear to the ground for growing industry trends or any other factors that might influence consumer behavior. Plan to revisit the source of your data frequently to determine if the sample set is representative of your future set and should continue to be used. Most importantly, regularly evaluate how your predictive analysis relates and contributes to your company’s overall goals and objectives.
What else do you think organizations can do to ward off the snags of predictive data analysis and use foresight more effectively?
by Elizabeth Hines | Apr 1, 2015 | Big Data, Blog, Data/Analytics, Logistics, Supply Chain
A distribution center struggling with a high number of forklift truck impacts found a way to leverage big data to address a nagging, costly warehouse issue. The company had installed a telematics solution on its forklift trucks, but could not determine the cause of the impacts. The time and location of impacts were known, as well as the identity of the drivers involved, but the company still needed to pull in more data sources for an effective assessment.
Forget for a moment the potential of adopting big data analytics throughout the entire supply chain and consider instead how big data can untangle and integrate seemingly unrelated masses of data to solve small problems in a warehouse or distribution center. That’s exactly what this company did.
By analyzing the link between environmental factors inside the distribution center and the forklift impact records, the culprit was swiftly identified: fast-moving thunderstorms that caused the humidity level to rise so quickly that the dehumidifiers could not keep up, increasing the risk of drivers losing control on the slippery concrete floor. That knowledge helped the company prevent sliding accidents by using a function of the telematics solution to reduce the maximum speed of the trucks when the humidity hit a certain level.
Indeed, distribution centers and warehouses present ideal environments — microcosms — for big data applications. Modern facilities are loaded with sensors and detectors to track every pallet and every piece of material handling equipment in real-time. Managers see the benefits in increased productivity, improved inventory flow, optimized equipment usage, and more. However, for that Eureka moment, organizations should also apply big data analytics across these multiple sources of data to uncover patterns that will drive even more, and perhaps surprising, operational improvements.
Rather than looking at data in isolation, a holistic approach holds significantly more power. Managers typically keep careful track of the performance of lift trucks, batteries, and chargers. But it is not until those entities are reviewed as a single system and matched with data coming off the lift trucks that a new level of revelations can be had.
Look for big data analytics to further raise the IQ of our “smart” warehouses and DCs. Inbound Logistics sums it up this way: “Accessing the right information to make smart decisions in the warehouse is one main reason why the demand for big data has grown so much — and so rapidly — in the distribution sector.”
Do you think distribution center and warehouse managers do enough to leverage big data?
