AI News, About managers who love aggregations a bit too much
About managers who love aggregations a bit too much
Data-driven decision making seems to be the new holy grail in management, but can the numbers always be trusted? What is key in data-savvy businesses: the people, the right technology, or –
These questions become particularly urgent in the new economy as failing to embrace data can be a major growth impediment or worse, a dead sentence to the business.
Recently, I’ve had lunch with a friend whose job is to manage sales people. I enjoy our work-related talks: they nudge me to look at problems from the management perspective, so different to my usual consultant goggles.
Besides the typical descriptors like region, product, or salesman, there are few known details. The information is too scarce to fully elaborate on the underlying strategy, what worked and what haven’t. Management is deemed to operate in this highly generalised vision of business.
Regional management was given only its own market to worry about, hence its response to changes has become faster, more relevant, and more agile than any central body could produce.
We all know the opposite: today, classic analytics combined with the sophistication of the visualisation tools and the data processing engines give us unprecedented access to data.
Business Intelligence thrives in multidimensional information analysis: viewing data from different angles is its building principle. One angle could be reviewing sales on a regional level and comparing it to a moment in time: how much did we sell last month in France?
BI reports might be produced with meticulous care, but are understood only by few. Managers might appreciate the value of analytics, but are untrained to work with data and unable to ask the right questions to challenge somebody else’s product. In result, the technology is misinterpreted, misapplied, or at best used to back up somebody’s gut feeling.
They therefore make decisions based on somebody else’s perspective, or as the aforementioned manager, they reject the irrelevant data and go with their intuition. This business culture fails at empowering the merited decision makers to act on available data.
Data-driven decision making is the new catch-phrase among the management, but how is the data supposed to drive change if the system isn’t designed to allow change?
is the number one example of business ignorance. Most service providers (mobile networks being the flagship example) allow the customer to only deal with the first line support.
It’s not the new tools, more data, or a PhD-holder staff that will make the change happen. A system, a program, or a robot, designed to be most rational, capable, and failure-proof is put to action only if the environment it operates in allows it.
Big Data: The Management Revolution
“You can’t manage what you don’t measure.” There’s much wisdom in that saying, which has been attributed to both W.
Simply put, because of big data, managers can measure, and hence know, radically more about their businesses, and directly translate that knowledge into improved decision making and performance.
Before long, they developed algorithms to predict what books individual customers would like to read next—algorithms that performed better every time the customer responded to or ignored a recommendation.
As the tools and philosophies of big data spread, they will change long-standing ideas about the value of experience, the nature of expertise, and the practice of management.
Business executives sometimes ask us, “Isn’t ‘big data’ just another way of saying ‘analytics’?” It’s true that they’re related: The big data movement, like analytics before it, seeks to glean intelligence from data and translate that into business advantage.
For instance, our colleague Alex “Sandy” Pentland and his group at the MIT Media Lab used location data from mobile phones to infer how many people were in Macy’s parking lots on Black Friday—the start of the Christmas shopping season in the United States.
At the same time, the steadily declining costs of all the elements of computing—storage, memory, processing, bandwidth, and so on—mean that previously expensive data-intensive approaches are quickly becoming economical.
As more and more business activity is digitized, new sources of information and ever-cheaper equipment combine to bring us into a new era: one in which large amounts of digital information exist on virtually any topic of interest to a business.
Mobile phones, online shopping, social networks, electronic communication, GPS, and instrumented machinery all produce torrents of data as a by-product of their ordinary operations.
The data available are often unstructured—not organized in a database—and unwieldy, but there’s a huge amount of signal in the noise, simply waiting to be released.
We just have more data.” The second question skeptics might pose is this: “Where’s the evidence that using big data intelligently will improve business performance?” The business press is rife with anecdotes and case studies that supposedly demonstrate the value of being data-driven.
We conducted structured interviews with executives at 330 public North American companies about their organizational and technology management practices, and gathered performance data from their annual reports and independent sources.
But across all the analyses we conducted, one relationship stood out: The more companies characterized themselves as data-driven, the better they performed on objective measures of financial and operational results.
So does accurate information about flight arrival times: If a plane lands before the ground staff is ready for it, the passengers and crew are effectively trapped, and if it shows up later than expected, the staff sits idle, driving up costs.
So when a major U.S. airline learned from an internal study that about 10% of the flights into its major hub had at least a 10-minute gap between the estimated time of arrival and the actual arrival time—and 30% had a gap of at least five minutes—it decided to take action.
It calculated these times by combining publicly available data about weather, flight schedules, and other factors with proprietary data the company itself collected, including feeds from a network of passive radar stations it had installed near airports to gather data about every plane in the local sky.
Every 4.6 seconds it collects a wide range of information about every plane that it “sees.” This yields a huge and constant flood of digital data.
PASSUR believes that enabling an airline to know when its planes are going to land and plan accordingly is worth several million dollars a year at each airport.
couple of years ago, Sears Holdings came to the conclusion that it needed to generate greater value from the huge amounts of customer, product, and promotion data it collected from its Sears, Craftsman, and Lands’ End brands.
It took so long mainly because the data required for these large-scale analyses were both voluminous and highly fragmented—housed in many databases and “data warehouses” maintained by the various brands.
This is simply a group of inexpensive commodity servers whose activities are coordinated by an emerging software framework called Hadoop (named after a toy elephant in the household of Doug Cutting, one of its developers).
The PASSUR and Sears Holding examples illustrate the power of big data, which allows more-accurate predictions, better decisions, and precise interventions, and can enable these things at seemingly limitless scale.
We’ve seen big data used in supply chain management to understand why a carmaker’s defect rates in the field suddenly increased, in customer service to continually scan and intervene in the health care practices of millions of people, in planning and forecasting to better anticipate online sales on the basis of a data set of product characteristics, and so on.
When data are scarce, expensive to obtain, or not available in digital form, it makes sense to let well-placed people make decisions, which they do on the basis of experience they’ve built up and patterns and relationships they’ve observed and internalized.
Mauboussin, in this issue.) For particularly important decisions, these people are typically high up in the organization, or they’re expensive outsiders brought in because of their expertise and track records.
First, they can get in the habit of asking “What do the data say?” when faced with an important decision and following up with more-specific questions such as “Where did the data come from?,” “What kinds of analyses were conducted?,” and “How confident are we in the results?” (People will get the message quickly if executives develop this discipline.) Second, they can allow themselves to be overruled by the data;
They can only give you answers.” Companies won’t reap the full benefits of a transition to using big data unless they’re able to manage change effectively.
Companies succeed in the big data era not simply because they have more or better data, but because they have leadership teams that set clear goals, define what success looks like, and ask the right questions.
On the contrary, we still must have business leaders who can spot a great opportunity, understand how a market is developing, think creatively and propose truly novel offerings, articulate a compelling vision, persuade people to embrace it and work hard to realize it, and deal effectively with customers, employees, stockholders, and other stakeholders.
The best data scientists are also comfortable speaking the language of business and helping leaders reformulate their challenges in ways that big data can tackle.
Why Knowledge Management Is Important To The Success Of Your Company
According to David Derbyshire, “Scientists have worked out exactly how much data is sent to a typical person in the course of a year - the equivalent of every person in the world reading 174 newspapers every single day” (Derbyshire, 2011, p.
Three key reasons why actively managing knowledge is important to a company’s success are: 1.) Facilitates decision-making capabilities, 2.) Builds learning organizations by making learning routine, and, 3.) Stimulates cultural change and innovation.
The CEC is composed of the heads of GE’s fourteen major businesses and the two-day sessions are forums for sharing best practices, accelerating progress, and discussing successes, failures, and experiences (Garvin, 2000, p.
While information overload or needing knowledge from people in other parts of the company for decision-making can handicap managers, putting in place knowledge management systems can facilitate better, more informed decisions.
In this complex, global business environment, these types of knowledge management programs can help managers embrace change and encourage ideas and insight, which often lead to innovation, even for local mom and pop business owners.
Good Data Won’t Guarantee Good Decisions
The ability to gather, store, access, and analyze data has grown exponentially over the past decade, and companies now spend tens of millions of dollars to manage the information streaming in from suppliers and customers.
To help organizations measure and improve employees’ facility with data-driven decision making, Corporate Executive Board created the Insight IQ, which assesses the ability to find and analyze relevant information.
“Informed skeptics”—the employees best equipped to make good decisions—effectively balance judgment and analysis, possess strong analytic skills, and listen to others’ opinions but are willing to dissent.
Our analysis also showed that functions whose employees had the highest average scores performed about 24% better than other functions across a wide range of metrics, including effectiveness, productivity, employee engagement, and market-share growth.
When a new form of analytics enters the workplace, companies typically start by hiring experts versed in using it, reasoning that the skills will trickle down to all.
Most IT functions “grew up” working with finance, supply chain, and HR, where business needs are clearly defined, stable, and relatively consistent over a wide group of users.
Companies that want to make better use of the data they gather should focus on two things: training workers to increase their data literacy and more efficiently incorporate information into decision making, and giving those workers the right tools.
Instead of simply answering questions as they arise, people-oriented data experts can provide informal, ongoing training to employees in departments outside their own, increasing the organization’s overall Insight IQ.
Companies introducing a new data-analysis tool often conduct one-off workshops that are overly focused on the tool itself, instead of on how managers can use it to improve their judgment—and because the training isn’t repeated, it’s apt to be quickly forgotten.
Tiffany holds year-round workshops that teach employees to use broad categories of information (such as sales, merchandising, and financial data) and instruct them in creating useful queries and employing analytical techniques.
As a result, they are better equipped to exploit information, and the IT team spends more time helping them derive value from the company’s data and less time answering simple data-support questions.
To understand how many “business intelligence” tools BCBSNC required, the IT team identified 10 skills that knowledge workers need in order to collect, analyze, and display information for decision making.
Top 7 decision-making tips for managers
People tend to choose the status quo over change, to stay in their comfort zone. But being comfortable with an approach may not be enough to justify it. Question whether you would choose a course of action if you weren't already following it. Examine your options as realistically as possible. Don't overstate the cost or the effort involved in making a change.
For example, if you were starting over, would you use the same marketing tactics to attract customers? Would you attend the same trade shows? Would you emphasize web-based marketing, direct mail or a mix? Don't forget to find supporting data that will help you review your choices objectively.
If you want to consult others about a problem, be sure to consider it carefully from as many angles as possible before talking to them. That way, you will avoid being limited by their interpretations and ideas. Frame the problem in as many ways as you can, and then seek out others to see whether they can add to your understanding of the issue.
Making sound decisions means taking into account the evidence that is available at the time. Sometimes the context changes and that decision is no longer valid. Recognize that you made the best decision possible under the circumstances, and then review the situation to see whether a different decision is now called for.
Avoid This Pitfall In Data-Driven Decisions In A Digital Transformation
When organizations start looking at their business through the lens of data instead of the lens of a business process, serious issues arise.
In a pilot project involving a cement-mixing plant, they gathered the sensor data and built algorithms to determine if the plant was running in the most efficient manner.
My experience is that companies typically go through one of two outcomes from this type of incident: As the construction firm recognized, building sustainable change means a company must work with the business unit stakeholders and let them see the data first before showing data to management and making it available online.
The typical change management tools and methodologies companies are accustomed to using are inadequate for company-wide, cross-functional change in a business model change.
Inadequate change management and failing to achieve buy-in of all stakeholders at the beginning of a digital initiative can cause delays or even cause an initiative to fail.
In the case of the construction firm, the company invested a tremendous amount of time, effort and capital in implementing new digital technologies for its IoT initiative, including building profiles and analytical frameworks so the company can interpret the data into signals that provide useful information.
Achieving the benefit of making decisions based on data instead of business processes would require a business model change and, consequently, significant change management.
- On 19. september 2020
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