Use the following as a checklist for your next abstract:.
Introduction
An abstract must be a fully self-contained, capsule description of the paper. It can't assume or attempt to provoke the reader into flipping through looking for an explanation of what is meant by some vague statement. It must make sense all by itself. Some points to consider include:. Writing an efficient abstract is hard work, but will repay you with increased impact on the world by enticing people to read your publications. Make sure that all the components of a good abstract are included in the next one you write. Prior research suggests digital technologies enable rapid, flexible forms of project organizing.
This research analyses practices of managing change in Airbus, CERN and Crossrail, through desk-based review, interviews, visits and a cross-case workshop. These organizations deliver complex projects, rely on digital technologies to manage large data-sets; and use configuration management, a systems engineering approach with midth century origins, to establish and maintain integrity.
Advanced data analytics is a means to an end. That answer will of course look different in different companies, industries, and geographies, whose relative sophistication with advanced data analytics is all over the map. And it will mean that different types of data should be isolated, aggregated, and analyzed depending upon the specific use case.
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Sometimes, data points are hard to find, and, certainly, not all data points are equal. The precise question your organization should ask depends on your best-informed priorities. Iterate through to actual business examples, and probe to where the value lies. One large financial company erred by embarking on just that sort of open-ended exercise: When findings emerged that were marginally interesting but monetarily insignificant, the team refocused.
With strong C-suite support, it first defined a clear purpose statement aimed at reducing time in product development and then assigned a specific unit of measure to that purpose, focused on the rate of customer adoption. A sharper focus helped the company introduce successful products for two market segments. Management has since begun to clarify its most pressing issues. But the world is rarely patient.
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Had these organizations put the question horse before the data-collection cart, they surely would have achieved an impact sooner, even if only portions of the data were ready to be mined. For example, a prominent automotive company focused immediately on the foundational question of how to improve its profits.
It then bore down to recognize that the greatest opportunity would be to decrease the development time and with it the costs incurred in aligning its design and engineering functions.
From outputs to action
The smallest edge can make the biggest difference. Consider the remarkable photograph below from the Olympics , taken at the starting line of the meter dash. Only one of the runners, Thomas Burke, crouched in the now-standard four-point stance. The race began in the next moment, and 12 seconds later Burke took the gold; the time saved by his stance helped him do it.
Today, sprinters start in this way as a matter of course—a good analogy for the business world, where rivals adopt best practices rapidly and competitive advantages are difficult to sustain. The good news is that intelligent players can still improve their performance and spurt back into the lead. Easy fixes are unlikely, but companies can identify small points of difference to amplify and exploit. If an organization can atomize a single process into its smallest parts and implement advances where possible, the payoffs can be profound.
And if an organization can systematically combine small improvements across bigger, multiple processes, the payoff can be exponential. Just about everything businesses do can be broken down into component parts.
Making data analytics work for you--instead of the other way around | McKinsey & Company
GE embeds sensors in its aircraft engines to track each part of their performance in real time, allowing for quicker adjustments and greatly reducing maintenance downtime. But if that sounds like the frontier of high tech and it is , consider consumer packaged goods. We know a leading CPG company that sought to increase margins on one of its well-known breakfast brands. It deconstructed the entire manufacturing process into sequential increments and then, with advanced analytics, scrutinized each of them to see where it could unlock value.
In this case, the answer was found in the oven: When a series of processes can be decoupled, analyzed, and resynched together in a system that is more universe than atom, the results can be even more powerful. A large steel manufacturer used various analytics techniques to study critical stages of its business model, including demand planning and forecasting, procurement, and inventory management. In each process, it isolated critical value drivers and scaled back or eliminated previously undiscovered inefficiencies, for savings of about 5 to 10 percent.
Those gains, which rested on hundreds of small improvements made possible by data analytics, proliferated when the manufacturer was able to tie its processes together and transmit information across each stage in near real time.
You Don’t Need a Data Scientist, You Need a Data Culture
By rationalizing an end-to-end system linking demand planning all the way through inventory management, the manufacturer realized savings approaching 50 percent—hundreds of millions of dollars in all. In reality, useful data points come in different shapes and sizes—and are often latent within the organization, in the form of free-text maintenance reports or PowerPoint presentations, among multiple examples.
But we can achieve sharper conclusions if we make use of fuzzier stuff. We understand that there are very few sure things; we weigh probabilities, contemplate upsides, and take subtle hints into account. Think about approaching a supermarket queue, for example. Do you always go to register four? Or do you notice that, today, one worker seems more efficient, one customer seems to be holding cash instead of a credit card, one cashier does not have an assistant to help with bagging, and one shopping cart has items that will need to be weighed and wrapped separately?
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In fact, while hard and historical data points are valuable, they have their limits. One company we know experienced them after instituting a robust investment-approval process. Understandably mindful of squandering capital resources, management insisted that it would finance no new products without waiting for historical, provable information to support a projected ROI.
Unfortunately, this rigor resulted in overly long launch periods—so long that the company kept mistiming the market. It was only after relaxing the data constraints to include softer inputs such as industry forecasts, predictions from product experts, and social-media commentary that the company was able to get a more accurate feel for current market conditions and time its product launches accordingly.