It simply means 'you have been doing a great job. Keep it up or keep up the good work: These phrases are usually said at the end of a conversation after having spoken about the good things that someone has done. To keep: zachowywa niedok.
Bly-fiks تمارين المحافظَه على اللياقَه البدنيَّه поддържане във форма manutenção kondiční cvičení kondiční sich gesund halten motionering motions- γυμναστική, γυμναστικός ejercicios de mantenimiento rütmivõimlemine نرمش؛ ورزش سبک kuntoilu gymnastique לִשמוֹר עַל כּוֹשֶר कसरत održavanje forme konditorna sistem latihan leikfimi (esercizi di ginnastica per mantenersi in forma) 美容体操 건강 유지 kasdienė mankšta veselības vingrojumi menjaga kesihatan conditietraining fysisk trening, kondisjonstrening kondisjons- ćwiczenia sprawnościowe پاسته او متناسب،يو ډول سپورت manutenção (de) gimnastică зарядка gymnastika gymnastický kondicijska telovadba kondicijski ostati fit motion การรักษาตัว formunu koruma 健身運動 підтримання фізичної форми ایک خاص قسم کی ورزش giữ gìn sức khỏe 体操 ˈkeepsake ( -seik) nounTo have everything one's neighbours have. So far, so good I’ve lost 160 pounds and am keeping it offKeep up the good work. In January 2010, I began eating better and exercising more, so I could lose weight. I live in Michigan, and I’m 42 years old. Meaning, pronunciation, translations. Is used as a congratulatory remark.
Keep It Up Series Or System
Tom Davenport shows you how to become an intelligent consumer of analytics so that you can make effective data-driven decisions for your organization.Learning basic statistical principles and methods is essential, but you don’t need a PhD in the stuff. Analytics are a competitive necessity nowadays, but hiring “quants” who can manipulate big data successfully is not enough. Comite's, so I have the luxury of having lived this advice for the past 12 months. Feeling Stronger (High Maintenance Remix) Keep It Up is a well written book that debunks the hype and fear around hormone therapy. To keep on: nie przerywa niedok. Collins COBUILD English Usage © HarperCollins Publishers 1992, 2004, 2011, 2012 keep= maintain in a certain state, place etc → halten to keep somebody at work → jdn bei der Arbeit halten he kept his hands in his pockets → er hat die Hände in der Tasche gelassen to keep good health → sich guter Gesundheit erfreuen the garden was well kept → der Garten war (gut) gepflegt? to keep sb/sth doing sth to keep somebody waiting → jdn warten lassen keep her thinking that … → lassen Sie sie in dem Glauben, dass … can’t you keep him talking? → können Sie ihn nicht in ein Gespräch verwickeln? to keep the traffic moving → den Verkehr in Fluss or am Fließen halten to keep a machine running → eine Maschine laufen lassen to keep the conversation going → das Gespräch in Gang halten keep it going! (Brit) → leg dich ins Zeug! (inf), → gib alles! (inf)? to keep sb/sth + ADJ to keep one’s dress clean → sein Kleid nicht schmutzig machen to keep somebody quiet → zusehen or dafür sorgen, dass jd still ist that’ll keep them quiet for a while → das wird für eine Weile Ruhe schaffen just to keep her happy → damit sie zufrieden ist to keep somebody alive → jdn am Leben halten to keep oneself busy → sich selbst beschäftigen to keep oneself warm → sich warm halten(= nag) → herumnörgeln an (+dat) keep at him until he says yes → lass ihm so lange keine Ruhe, bis er ja sagt vt +prep obj to keep somebody at a task → jdn nicht mit einer Arbeit aufhören lassen to keep somebody (hard) at it → jdn hart rannehmen (inf), → jdn an der Kandare haben they kept him at it all day → sie haben ihn den ganzen Tag hart rangenommen (inf)? keep away vi (lit) → wegbleiben (= not approach) → nicht näher herankommen (→ from an +acc) keep away! → nicht näher kommen! keep away from that place → gehen Sie da nicht hin he just can’t keep away from the pub → es zieht ihn immer wieder in die Wirtschaft I just can’t keep away → es zieht mich immer wieder hin keep away from him → lassen Sie die Finger von ihm he just can’t keep away from her → er kann einfach nicht von ihr lassen vt (always separate) person, children, pet etc → fernhalten ( from von) to keep something away from something → etw nicht an etw (acc) → kommen lassen keep your hand away from the cutting edge → kommen Sie mit Ihrer Hand nicht an die Schneide keep them away from each other → halten Sie sie auseinander to keep somebody away from school → jdn nicht in die Schule (gehen) lassen business kept him away for three months → er war aus geschäftlichen Gründen drei Monate weg what’s been keeping you away? → wo waren Sie denn so lange? keep back vi → zurückbleiben, nicht näher kommen keep back! → bleiben Sie, wo Sie sind!, treten Sie zurück! please keep back from the edge → bitte gehen Sie nicht zu nahe an den Rand vt sepA series or system of exercises, usually simple, intended to improve the physical condition of ordinary people, especially women.
But as the banking example shows, having big data—and even people who can manipulate it successfully—is not enough. Whether you work in financial services, consumer goods, travel and transportation, or industrial products, analytics are becoming a competitive necessity for your organization. I didn’t even know his group was working on repayment probabilities.” The bank ended up losing billions in bad loans.We live in an era of big data. “I had a model strongly indicating that a lot of them wouldn’t be repaid, and I sent it to the head of our mortgage business.”When I asked the leader of the mortgage business why he’d ignored the advice, he said, “If the analyst showed me a model, it wasn’t in terms I could make sense of. Bank told me a few years ago.
You, the ConsumerStart by thinking of yourself as a consumer of analytics. You might be adept at using spreadsheets and know your way around a bar graph or a pie chart, but when it comes to analytics, you often feel quantitatively challenged.So what does the shift toward data-driven decision making mean for you? How do you avoid the fate of the loss-making mortgage bank head and instead lead your company into the analytical revolution, or at least become a good foot soldier in it? This article—a primer for non-quants—is based on extensive interviews with executives, including some with whom I’ve worked as a teacher or a consultant. But if you’re a typical executive, your math and statistics background probably amounts to a college class or two.
Your job as a data consumer—to generate hypotheses and determine whether results and recommendations make sense in a changing business environment—is therefore critically important. But most lack sufficient knowledge to identify hypotheses and relevant variables and to know when the ground beneath an organization is shifting. Producers are, of course, good at gathering the available data and making predictions about the future.
Analytics-Based Decision Making—in Six Key StepsWhen using big data to make big decisions, non-quants should focus on the first and the last steps of the process. (See the sidebar “Analytics-Based Decision Making—in Six Key Steps.”) As the famous statistician George Box noted, “All models are wrong, but some are useful.” In other words, models intentionally simplify our complex world. You need to understand the process for making analytical decisions, including when you should step in as a consumer, and you must recognize that every analytical model is built on assumptions that producers ought to explain and defend. If not, bone up on the basics of regression analysis, statistical inference, and experimental design. Learn a little about analytics.If you remember the content of your college-level statistics course, you may be fine. Some require only changes in attitude and perspective others demand a bit of study.
Present and act on the resultsUse the data to tell a story to decision makers and stakeholders so that they will take action.To become more data literate, enroll in an executive education program in statistics, take an online course, or learn from the quants in your organization by working closely with them on one or more projects.No matter how much you trust your quants, don’t stop asking them tough questions.Jennifer Joy, the vice president of clinical operations at Cigna, took the third approach. Analyze the dataRun a statistical model, assess its appropriateness for the data, and repeat the process until a good fit is found. Collect the dataGather primary and secondary data on the hypothesized variables. Model the solution and select the variablesFormulate a detailed hypothesis about how particular variables affect the outcome. Review previous findingsIdentify people who have tried to solve this problem or similar ones—and the approaches they used. Recognize the problem or questionFrame the decision or business problem, and identify possible alternatives to the framing.
Each pilot lasts just a couple of months, and multiple studies are run simultaneously—so Joy now gets information about the effectiveness of her programs on a rolling basis.In the end, Joy and her quant partners learned that the coaching worked for people with certain diseases but not for other patients, and some call center staff members were redeployed as a result. Specifically, she uses analytics to “prematch” pairs of patients and then to randomly assign one member of the pair to receive those services, while the other gets an alternative such as a mail-order or an online-support intervention. She learned, for example, that she could conduct pilot studies to discover which segments of her targeted population benefit the most (and which the least) from her call center’s services. She knew, however, that the voluminous reports she received about her call center operations weren’t telling her whether the coaching calls made to patients were actually helping to manage their diseases and to keep them out of the hospital.So Joy reached out to Cigna’s analytics group, in particular to the experts on experimental design—the only analytical approach that can potentially demonstrate cause and effect.
Keep It Up Plus Branches Serving
For several years she’s been pushing her direct reports to use analytics to make better decisions—for example, about which branches to open or close, how to reduce customer wait times, what incentives lead to multichannel interactions, and why some salespeople are more productive than others.Bank of America has hundreds of quants, but most of them were pooled in a group that managers could not easily access. As the head of retail strategy and distribution for the bank’s consumer division, she oversees 5,400-plus branches serving more than 50 million consumers and small businesses. As one wag jokingly advised, “Look for the quants who stare at your shoes, instead of their own, when you engage them in conversation.” But it’s possible to find people who communicate well and have a passion for solving business—rather than mathematical—problems and, after you’ve established a relationship, to encourage frank dialogue and data-driven dissent between the two of you.Katy Knox, at Bank of America, has learned how to align with data producers. Research and analytics at Cigna, attests, she’s learned to be “very analytically oriented.” Align yourself with the right kind of quant.Karl Kempf, a leader in Intel’s decision-engineering group, is known at the company as the “überquant” or “chief mathematician.” He often says that effective quantitative decisions “are not about the math they’re about the relationships.” What he means is that quants and the consumers of their data get much better results if they form deep, trusting ties that allow them to exchange information and ideas freely.Of course, highly analytical people are not always known for their social skills, so this can be hard work. She may not understand all the methodological details, but as Michael Cousins, the vice president of U.S.
She worked especially closely with two team leaders, Justin Addis and Michael Hyzy, who have backgrounds in retail banking and Six Sigma, so they’re able to understand her unit’s business problems and communicate them to the hard-core quants they manage.
0 kommentar(er)
