Digital Learning Kit

Learning Technologies Group

KNOWLEDGE

A1�

'dkYwm0efta&;oHk;yg;'dkYwm0efta&;oHk;yg;

'dkYwm0efta&;oHk;yg;'dkYwm0efta&;oHk;yg;'dkYwm0efta&;oHk;yg;

�jynfaxmifpk rNydKuGJa&; 'dkYta&;

�wdkif;&if;om;pnf;vHk;nDnTwfrI rNydKuGJa&; 'dkYta&;

�tcsKyftjcmtmPm wnfhwHhcdkifNrJa&; 'dkYta&;

jynfolYoabmxm;jynfolYoabmxm;

jynfolYoabmxm;jynfolYoabmxm;jynfolYoabmxm;

�jynfytm;udk; ykqdef½dk; tqdk;jrif0g'Drsm;tm;qefYusifMu/

�EdkifiHawmfwnfNidrfat;csrf;a&;ESifh EdkifiHawmfwdk;wufa&;udk

aESmuf,Suf zsufqD;olrsm;tm; qefYusifMu/

�EdkifiHawmf\jynfwGif;a&;udk 0ifa&mufpGufzufaESmifh,Sufaom

jynfy EdkifiHrsm;tm;qefYusifMu/

�jynfwGif;jynfy tzsuform;rsm;tm; bHk&efoltjzpfowfrSwf

acsrIef;Mu/

EdkifiHa&;OD;wnfcsuf (4) &yfEdkifiHa&;OD;wnfcsuf (4) &yf

EdkifiHa&;OD;wnfcsuf (4) &yfEdkifiHa&;OD;wnfcsuf (4) &yfEdkifiHa&;OD;wnfcsuf (4) &yf

�EdkifiHawmfwnfNidrfa&;? &yf&Gmat;csrf;om,ma&;ESifhw&m;

Oya'pdk;rdk;a&;/

�trsdK;om;jyefvnfpnfvHk;nDnGwfa&;/

�cdkifrmonfhzGJYpnf;yHktajccHOya'opfjzpfay:vma&;/

�jzpfay:vmonfhzGJYpnf;yHktajccHOya'opfESifhtnD acwfrDzGYHNzdK;

wdk;wufaom EdkifiHawmfopfwpf&yf wnfaqmufa&;/

pD;yGm;a&;OD;wnfcsuf (4) &yfpD;yGm;a&;OD;wnfcsuf (4) &yf

pD;yGm;a&;OD;wnfcsuf (4) &yfpD;yGm;a&;OD;wnfcsuf (4) &yfpD;yGm;a&;OD;wnfcsuf (4) &yf

�pdkufysdK;a&;udktajccHí tjcm;pD;yGm;a&; u@rsm;udkvnf;

bufpHk zGYHNzdK;wdk;wufatmif wnfaqmufa&;/

�aps;uGufpD;yGm;a&;pepf yDjyifpGmjzpfay:vma&;/

�jynfwGif;jynfyrS twwfynmESifh t&if;tESD;rsm;zdwfac:í

pD;yGm;a&; zGYHNzdK;wdk;wufatmif wnfaqmufa&;/

�EdkifiHawmfpD;yGm;a&; wpf&yfvHk;udk zefwD;EdkifrIpGrf;tm;onf

EdkifiHawmfESifh wdkif;&if;om;jynfolwdkY\ vuf0,fwGif&Sda&;/

vlrIa&;OD;wnfcsuf (4) &yfvlrIa&;OD;wnfcsuf (4) &yf

vlrIa&;OD;wnfcsuf (4) &yfvlrIa&;OD;wnfcsuf (4) &yfvlrIa&;OD;wnfcsuf (4) &yf

�wpfrsdK;om;vHk;\ pdwf"mwfESifhtusifhpm&dwÅ jrifhrm;a&;/

�trsdK;*kPf? Zmwd*kPfjrifhrm;a&;ESifh ,Ofaus;rItarGtESpfrsm;?

trsdK;om;a&; vu©Pmrsm; raysmufysufatmifxdef;odrf;

apmifha&Smufa&;?

�rsdK;cspfpdwf"mwf &Sifoefxufjrufa&;/

�wpfrsdK;om;vHk;usef;rmBuHhcdkifa&;ESifh ynm&nfjrifhrm;a&;/


KNOWLEDGE

A1�

þ Investing , Business, Management & Personal Development

Digital Learning Kit ( DLK ) onf jrefrmEd ki fiH\ Human

Resource Development tm;wpfzufwpfvrf;rS axmufyHhay;vdkaom

&nf½G,fcsufjzifh pmzwfolrsm;xHodkU ]]tcr]]tcr

]]tcr]]tcr]]tcrJh}} jzefUa0jcif;jzpfygonf/

xdkUaMumifh rnfolrqdkþ ( DLK ) wGif yg0ifaom taMumif;t&m

rsm;tm; vufqifhurf;jzefUa0jcif;jzifh ukodkvf,lEdkifygonf/

]]vufqifhurf;jzefUa0ay;olrsm;tm;txl;yifaus;Zl;wif½Sdygonf/}} ]]vufqifhurf;jzefUa0ay;olrsm;tm;txl;yifaus;Zl;wif½Sdygonf/}}

]]vufqifhurf;jzefUa0ay;olrsm;tm;txl;yifaus;Zl;wif½Sdygonf/}} ]]vufqifhurf;jzefUa0ay;olrsm;tm;txl;yifaus;Zl;wif½Sdygonf/}} ]]vufqifhurf;jzefUa0ay;olrsm;tm;txl;yifaus;Zl;wif½Sdygonf/}}

odkUaomf rdrd\ukd,fusdK;pD;yGm;twGuf þ ( DLK ) tm;

vnf;aumif;? þ ( DLK ) wGifyg0ifaom taMumif;t&mrsm;tm;

vnf;aumif;? wdkuf½dkufjzpfap? oG,f0dkufíjzpfap? bmomjyefíjzpfap

a&mif;csjcif;rsm;udk rjyKMuyg&efav;pm;pGm arwÅm&yfcHtyfygonf/

KNOWLEDGE

Learning Technologies GroupA1�

A Step-by-Step Guide to Smart Business Experiments

by Eric T. Anderson and Duncan Simester

Over the past decade, managers have awakened to the power of analytics. Sophisticated computers andsoftware have given companies access to immense troves of data: According to one estimate, businessescollected more customer information in 2010 than in all prior years combined. This avalanche of data presentscompanies with big opportunities to increase profits—if they can find a way to use it effectively.

The reality is that most firms can’t. Analytics, which focuses on dissecting past data, is a complicated task. Fewfirms have the technical skills to implement a full-scale analytics program. Even companies that make big in-vestments in analytics often find the results difficult to interpret, subject to limitations, or difficult to use to immedi-ately improve the bottom line.

Most companies will get more value from simple business experiments. That’s because it’s easier to draw theright conclusions using data generated through experiments than by studying historical transactions. Manag-ers need to become adept at using basic research techniques. Specifically, they need to embrace the “test andlearn” approach: Take one action with one group of customers, take a different action (or often no action at all)with a control group, and then compare the results. The outcomes are simple to analyze, the data are easilyinterpreted, and causality is usually clear. The test-and-learn approach is also remarkably powerful. Feedbackfrom even a handful of experiments can yield immediate and dramatic improvements in profits. ( See the sidebar“How One Retailer Tested Its Discount Strategy.”) And unlike analytics, experimentation is a skill that nearly anymanager can acquire.

How One Retailer Tested Its Discount Strategy (Located at the end of this article)

Admittedly, it can be hard to know where to start. In this article, we provide a step-by-step guide to conductingsmart businessexperiments.

It’s All About Testing Customers’ Responses

In some industries, experimentation is already a way of life. The J. Crew or Pottery Barn catalog that arrives inyour mailbox is almost certainly part of an experiment—testing products, prices, or even the weight of the paper.Charitable solicitations and credit card offers are usually part of marketing tests, too. Capital One conducts tensof thousands of experiments each year to improve the way it acquires customers, maximizes their lifetime value,and even terminates unprofitable ones. In doing so, Capital One has grown from a small division of Signet Bankto an independent company with a market capitalization of $19 billion.

1

KNOWLEDGE


The ease with which companies can experiment depends on how easily they can observe outcomes. Direct-mail houses, catalog companies, and online retailers can accurately target individuals with different actions andgauge the responses. But many companies engage in activities or reach customers through channels thatmake it impossible to obtain reliable feedback. The classic example is television advertising. Coke can onlyguess at how viewers responded to its advertising during the last Olympics, a limitation recognized by JohnWanamaker’s famous axiom, “Half the money I spend on advertising is wasted; the trouble is, I don’t knowwhich half.” Without an effective feedback mechanism, the basis for decision making reverts to intuition.

In practice, most companies fall somewhere between these two extremes. Many are capable of conductingtests only at an aggregate level, and they’re forced to compare nonequivalent treatment and control groups toevaluate the response. If Apple wants to experiment with the prices of a new iPhone, it may be limited to chargingdifferent prices in different countries and observing the response. In general, it’s easier to experiment withpricing and product decisions than with channel management or advertising decisions. It’s also easier toexperiment in consumer settings than in business-to-business settings, because B2C markets typically havefar more potential customers to serve as subjects.

Think Like a Scientist

Running a business experiment requires two things: a control group and a feedback mechanism.

Though most managers understand the purpose of control groups in experimentation, many companies neglectto use them,rolling out tests of new offerings across their entire customer base. A company that wants to evaluate the effectof exclusivityon its dealer network, for instance, is missing an opportunity if it offers all its dealers exclusivity. It should maintainnonexclusivity in certain regions to make it easier to evaluate how exclusivity affects outcomes.

Ideally, control groups are selected through randomization. When Capital One wanted to test the effectivenessof free transfers of balances from other credit cards (the innovation that initially launched its success), it offeredthe promotion to a random sample of prospective customers, while a different random sample (the controlgroup) received a standard offer. Often it makes sense for a company to set up a treatment group and then usethe remainder of the customer base as a control group, as one bank did when it wanted to experiment with itsonline retail trading platform. That approach gave bank managers a very large sample of equivalent customersagainst which to evaluate the response to the new platform.

Overcoming Reluctance to Experiment (Located at the end of this article)

2

KNOWLEDGE


The key to success with treatment and control groups is to ensure separation between them so that the actionstaken with one group do not spill over to the other. That can be difficult to achieve in an online setting wherecustomers may visit your website repeatedly, making it challenging to track which versions of the site they wereexposed to. Separation can also be hard to achieve in traditional settings, where varying treatments acrossstores may lead to spillovers for customers who visit multiple stores. If you cannot achieve geographic separa-tion, one solution may be to vary your actions over time. If there is concern that changes in underlying demandmay confound the comparisons across time, consider repeating the different actions in multiple short timeperiods.

The second requirement is a feedback mechanism that allows you to observe how customers respond todifferent treatments. There are two types of feedback metrics: behavioral and perceptual. Behavioral metricsmeasure actions—ideally, actual purchases. However, even intermediate steps in the purchasing processprovide useful data, as Google’s success illustrates. One reason Google is so valuable to advertisers is that itenables them to observe behavioral expressions of interest—such as clicking on ads. If Google could mea-sure purchases rather than mere clicks, it would be even more valuable. Of course, Google and its competi-tors realize this and are actively exploring ways to measure the effects of advertising on purchasing decisionsin online and traditional channels.

Perceptual measures indicate how customers think they will respond to your actions. This speculative form offeedback is most often obtained via surveys, focus groups, conjoint studies, and other traditional forms ofmarket research. These measures are useful in diagnosing intermediate changes in customers’ decisionprocesses.

Given that the goal of most firms is to influence customers’ behavior rather than just their perceptions, experi-ments that measure behavior provide a more direct link to profit, particularly when they measure purchasingbehavior.

Seven Rules for Running Experiments

As with many endeavors, the best experimentation programs start with the low-hanging fruit—experiments thatare easy to implement and yield quick, clear insights. A company takes an action—such as raising or loweringa price or sending out a direct-mail offer—and observes customers’ reactions.

You can identify opportunities for quick-hit experiments at your company using these criteria.

1. Focus on individuals and think short term.

The most accurate experiments involve actions to individual customers, rather than segments or geographies,andobservations of their responses. The tests measure purchasing behavior (rather than perceptions) and revealwhether changes

3

KNOWLEDGE


lead to higher profits. Focus your experiments on settings in which customers respond immediately. WhenUBS was considering how to use experiments to improve its wealth management business, it recognized thatthe place to start was customer acquisition, not improving lifetime customer value. The effects of experimentson customer acquisitions can be measured immediately, while the impact on customer lifetime value couldtake 25 years to assess.

2. Keep it simple.

Look for experiments that are easy to execute using existing resources and staff. When a bank wanted to runa customer experiment, it didn’t start with actions that required retraining of retail tellers. Instead, it focused onactions that could be automated through the bank’s information systems. Experiments that require extensivemanipulation of store layout, product offerings, or employee responsibilities may be prohibitively costly. Weknow one retailer that ran a pricing experiment involving thousands of items across a large number of stores—a labor-intensive action that cost more than $1 million. Much of what the retailer learned from thatmammothexperiment could have been gleaned from a smaller test that used fewer stores and fewer productsand preserved resources for follow-up tests.

3. Start with a proof-of-concept test.

In academic experiments, researchers change one variable at a time so that they know what caused theoutcome. In a business setting, it’s important to first establish proof of concept. Change as many variables inwhatever combination you believe is most likely to get the result you want. When a chain of convenience storeswanted to test the best way to shift demand from national brands to its private-label brands, it increased theprices of the national brands and decreased the private-label brand prices. Once it established that shiftingdemand was feasible, the retailer then refined its strategy by varying each of the prices individually.

4. When the results come in, slice the data.

When customers are randomly assigned to treatment and control groups, and there are many customers ineach group, then you may effectively have multiple experiments to analyze. For example, if your sample in-cludes both men and women, you can evaluate the outcome with men and women separately. Most actionsaffect some customers more than others. So when the data arrive, look for subgroups within your control andtreatment groups. If you examine only aggregate data, you may incorrectly conclude that there no effects onany customers. (See the exhibit “ Slicing an Experiment.”)

Slicing an Experiment (Located at the end of this article)

The characteristics that you use to group customers, such as gender or historical purchasing patterns, mustbe independent of the action itself. For example, if you want to analyze how a store opening affects catalogdemand, you cannot simply compare customers who made a purchase at the store with customers who didnot. The results will reflect existing customer differences rather than the impact of opening the store. Consider

4

KNOWLEDGE


instead comparing purchases by customers who live close to the new store versus customers who live faraway. As long as the two groups are roughly equivalent, the differences in their behavior can be attributed tothe store opening.

5. Try out-of-the-box thinking.

A common mistake companies make is running experiments that only incrementally adjust current policies.For example, IBM may experiment with sales revenues by varying the wholesale prices that it offers to resellers.However, it may be more profitable to experiment with completely different sales approaches—perhaps involv-ing exclusive territories or cooperative advertising programs. If you never engage in “what-if” thinking, yourexperiments are unlikely to yield breakthrough improvements. A good illustration is provided by Tesco, the UKsupermarket chain. It reportedly discovered that it was profitable to send coupons for organic food to custom-ers who bought wild birdseed. This is out-of-the-box thinking. Tesco allows relatively junior analysts at its cor-porate headquarters to conduct experiments on small numbers of customers. These employees deliver some-thing that the senior managers generally don’t: a steady stream of creative new ideas that are relevant toyounger customers.

6. Measure everything that matters.

A caution about feedback measures: They must capture all the relevant effects. A large national apparel retailerrecently conducted a large-scale test to decide how often to mail catalogs and other promotions to differentgroups of customers. Some customers received 17 catalogs over nine months, whereas another randomlyselected group received 12 catalogs over the same time period. The retailer discovered that for its best cus-tomers the additional catalogs increased sales during the test period, but lowered sales in subsequent months.When the retailer compared sales across its channels, it found that its best customers purchased more oftenthrough the catalog channel (via mail and telephone) but less from its online stores. When the firm aggregatedsales across the different time periods and across its retail channels, it concluded that it could mail a lot lessfrequently to its best customers without sacrificing sales. Viewing results in context is critical whenever actionsin one channel affect sales in other channels or when short-term actions can lead to long-run outcomes. Thisis the reason that we recommend starting with actions that have only short-run outcomes, such as actions thatdrive customer acquisition.

7. Look for natural experiments.

The Norwegian economist Trygve Haavelmo, who won the 1989 Nobel Prize, observed that there are twotypes of experiments: “those we should like to make” and “the stream of experiments that nature is steadilyturning out from her own enormous laboratory, and which we merely watch as passive observers.” If firms canrecognize when natural experiments occur, they can learn from them at little or no additional expense. Forexample, when an apparel retailer opened its first store in a state, it was required by law to start charging salestax on online and catalog orders shipped to that state, whereas previously those purchases had been tax-free. This provided an opportunity to discover how sales taxes affected online and catalog demand.

5

KNOWLEDGE


The retailer compared online and catalog sales before and after the store opening for customers who lived oneither side of the state’s southern border, which was a long way from the new store. None of the customerswere likely to shop in the new store, so its opening would have no effect on demand—the only change was thetaxation of online and catalog purchases, which affected consumers only on one side of the border. Thecomparison revealed that the introduction of sales taxes led to a large drop in online sales but had essentiallyno impact on catalog demand.

The key to identifying and analyzing natural experiments is to find treatment and control groups that werecreated by some outside factor, not specifically gathered for an experiment. Geographic segmentation is onecommon approach for natural experiments, but it will not always be a distinguishing characteristic. For ex-ample, when GM, Ford, and Chrysler offered the public the opportunity to purchase new cars at employeediscount levels, there was no natural geographic separation—all customers were offered the deal. Instead, toevaluate the outcome of these promotions, researchers compared transactions in the weeks immediatelybefore and after the promotions were introduced. Interestingly, they discovered that the jump in sales levelswas accompanied by a sharp increase in prices. Customers thought they were getting a good deal, but inreality prices on many models were actually lower before the promotion than with the employee discountprices. Customers responded to the promotion itself rather than to the actual prices, with the result that manycustomers were happy with the deal, even though they were paying higher prices.

Avoid Obstacles

Companies that want to tap into the power of experimentation need to be aware of the obstacles—both exter-nal and internal ones. In some cases, there are legal obstacles: Firms must be careful when charging differentprices to distributors and retailers, particularly firms competing with one another. Although there are fewer legalramifications when charging consumers different prices (the person sitting next to you on your airline flight hasusually paid more or less than you), the threat of an adverse consumer reaction is a sufficient deterrent forsome firms. No one likes to be treated less favorably than others. This is particularly true when it comes toprices, and the widespread availability of price information online means that variations are often easily discov-ered.

The internal obstacles to experimentation are often larger than the external barriers. In an organization with aculture of decision making by intuition, shifting to an experimentation culture requires a fundamental change inmanagement outlook. Management-by-intuition is often rooted in an individual’s desire to make decisionsquickly and a culture that frowns upon failure. In contrast, experimentation requires a more measured deci-sion-making style and a willingness to try many approaches, some of which will not succeed.

Some companies mistakenly believe that the only useful experiments are successful ones. But the goal is notto conduct perfect experiments; rather, the goal is to learn and make better decisions than you are making rightnow. Without experimentation, managers generally base decisions on gut instinct. What’s surprising is not justhow bad those decisions typically are, but how good managers feel about them. They shouldn’t—there’susually a lot of room for improvement. Organizations that cultivate a culture of experimentation are often led by

6

KNOWLEDGE


senior managers who have a clear understanding of the opportunities and include experimentation as astrategic goal of the firm. This is true of Gary Loveman, the CEO of Harrah’s, now called Caesars Entertain-ment, who transformed the culture of a 35,000-employee organization to eventually enshrine experimentationas a core value. He invested in the people and infrastructure required to support experimentation and alsoenforced a governance mechanism that rewarded this approach. Decisions based solely upon intuition werecensured, even if the hunch was subsequently proved correct.

There is generally a practical limit on the number of experiments managers can run. Because of that, analyticscan play an important role, even at companies in which experiments drive decision making. When CapitalOne solicits new cardholders by mail, it can run thousands of experiments; there’s no need to pretest theexperiments by analyzing historical data. But other companies’ business models may allow for only a fewexperiments; in such cases, managers should carefully plan and pretest experiments using analytics. Forexample, conducting experiments in channel settings is difficult because changes involve confrontation anddisruption of existing relationships. This means that most firms will be limited in how many channel experi-ments they can run. In these situations, analyzing historic data, including competitors’ actions and outcomes inrelated industries, can offer valuable initial insights that help focus your experiments.

Whether the experiments are small or large, natural or created, your goal as a manager is the same: to shiftyour organization from a culture of decision making by intuition to one of experimentation. Intuition will continueto serve an important role in innovation. However, it must be validated through experimentation before ideassee widespread implementation. In the long run, companies that truly embrace this data-driven approach willbe able to delegate authority to run small-scale experiments to even low levels of management. This will en-courage the out-of-the-box innovations that lead to real transformation.

How One Retailer Tested Its Discount Strategy

Walk into any large retail store, and you’ll find price promotions being offered on big national brands—dis-counts that are funded by the manufacturer. For retailers, these promotions can be a mixed bag: Although thelower prices may increase sales of the item, the promotion may hurt sales of competing private-label products,which offer higher margins. We worked with one national retailer that decided to conduct experiments to deter-mine how it might shield its private-label market share by promoting these products while the national brandswere on sale.

The retailer designed six experimental conditions—a control and five discount levels that ranged from zero to35% for the private-label items. The retailer divided its stores into six groups, and the treatments were random-ized across the groups. This meant each store had a mixture of the experimental conditions distributed acrossthe different products in the study. For example, in Store A private-label sugar was discounted 20%, and pri-vate-label mascara was full price, whereas in Store B mascara was discounted, but sugar was not. Thisexperimental design allowed the retailer to control for variations in sales that occurred because the storegroups were not identical.

7

KNOWLEDGE


The test revealed that matching the national brand promotions with moderate discounts on the private-labelproducts generated 10% more profits than not promoting the private-label items. As a result, the retailer nowautomatically discounts private-label items when the competing national brands are under promotion. Afterestablishing proof of concept, it refined its shielding policies by testing responses to various types of nationalbrand promotions. For example, it discovered that a “Buy One, Get One for 50% Off” promotion on a nationalbrand should also be matched with the same offer on the private label, rather than just a straight discount.

These experiments were successful for two main reasons: The actions were easy to implement, and theresults were easy to measure. Each set of experiments lasted just one week. There were few enough prod-ucts involved that stores did not require any additional labor. The experiments piggybacked on the standardprocedures for promoting an item—indeed, the store employees were unaware that they were helping toimplement an experiment.

In previous experiments the retailer had learned that if it changed too many things at once, the stores could nothandle the implementation without long delays and a lot of additional cost. In some cases temporary labor hadto be trained to go into the stores, find the products, and change the prices and shelf signage. Moreover, if theexperiment extended beyond a week, problems arose as shelves were constantly rearranged and new signsapplied. A maintenance program was required to monitor store compliance. In effect the retailer experimentedon experimentation itself—it learned how to design studies that it could analyze more quickly and implementmore easily.

Overcoming Reluctance to Experiment

A large bank we worked with decided to use experiments to improve the way it advertised its certificates ofdeposit, a core product. In the past, decisions on ads had been made largely by a single manager, whoseextensive experience endowed him with power and status within the organization—and a big salary.

The possibility that the bank would use experiments to supplant his intuitive decision making was a threat to themanager. Not surprisingly, he obstructed the process, arguing that planning lead times were too long anddecisions had already been made. A senior leader whose P&L was directly affected by the advertising deci-sions had to intervene. He allowed the experiments to go forward—and reassured his team that any misstepsresulting from the experiments would not affect their year-end bonuses.

Organizational recalcitrance is one of the key hurdles companies encounter when trying to create a culture ofexperimentation. The main obstacle to establishing the new usual is the old usual. Organizations have theirways of making decisions, and changing them can be a formidable challenge.

One mistake some firms make is to delegate experimentation to a customer intelligence group. This grouphas to lobby each business unit for the authority to conduct experiments. That’s the wrong approach: Experi-ments are designed to improve decision making, and so responsibility for them must occur where those deci-sions are made—in the business units themselves.

8

KNOWLEDGE


It is also important to set the right expectations. It’s a mistake to expect every experiment to discover a moreprofitable approach—perhaps only 5% of them will do that. Those odds mean that taking eight months toimplement a single large-scale experiment is a bad strategy. Productive experimentation requires an infra-structure to support dozens of small-scale experiments. Of perhaps 100 experiments, only five to 10 will lookpromising and can be replicated, yielding one to two actions that are almost certain to be profitable. Focus yourorganization on these and scale them hard. Your goal, at least initially, is to find the golden ticket—you’re notlooking for lots of small wins.

Golden tickets can be hard to find, but that’s largely because most organizations lack the perseverance toovercome the institutional resistance that stands in the way of discovering them.

Slicing an Experiment

When you’re conducting an experiment, it’s important to remember that initial results may be deceiving. Con-sider a publishing company that tried to assess how discounts affect customers’ future shopping behavior. Itmailed a control group of customers a catalog containing a shallow discount—its standard practice. The treat-ment group of customers received catalogs with deep discounts on certain items. For two years, the companytracked purchases at an aggregate level, and the difference between the two groups was negligible:

But that view of the data did not tell the whole story. Further analysis revealed a disturbing outcome amongcustomers who had recently purchased a high-priced item and then received a catalog offering the same itemat a 70% discount. Apparently upset by this perceived overcharge, these customers (some of them long-standing ones) cut future spending by 18%:

9

KNOWLEDGE


Upon learning these results, the publishing firm modified its direct-mail approach to avoid inadvertently antago-nizing its best customers.

Eric T. Anderson is the Hartmarx Professor of Marketing at Northwestern’s Kellogg School of Management.Duncan Simester is the NTU Professor of Management Science at MIT’s Sloan School of Management.

10

11

KNOWLEDGE


Corporate Boards That Create Value E-book (tydkif; - 3) (tydkif; - 3)

(tydkif; - 3) (tydkif; - 3) (tydkif; - 3)

12

KNOWLEDGE


13

KNOWLEDGE


14

KNOWLEDGE


15

KNOWLEDGE


16

KNOWLEDGE


17

KNOWLEDGE


18

KNOWLEDGE


19

KNOWLEDGE


20

KNOWLEDGE


21

KNOWLEDGE


22

KNOWLEDGE


23

KNOWLEDGE


24

KNOWLEDGE


25

KNOWLEDGE


26

KNOWLEDGE


27

KNOWLEDGE


28

KNOWLEDGE


29

KNOWLEDGE


30

KNOWLEDGE


31

KNOWLEDGE


32

KNOWLEDGE


33

KNOWLEDGE


34

KNOWLEDGE


35

KNOWLEDGE


36

KNOWLEDGE


37

KNOWLEDGE


38

KNOWLEDGE


39

KNOWLEDGE


40

KNOWLEDGE


41

KNOWLEDGE


Next issue wGifqufvufzwf&Iyg&efwGifqufvufzwf&Iyg&ef

wGifqufvufzwf&Iyg&efwGifqufvufzwf&Iyg&efwGifqufvufzwf&Iyg&ef

42

43

KNOWLEDGE


ates\ pDrHcefUcGJrI\ pDrHcefUcGJrI

\ pDrHcefUcGJrI\ pDrHcefUcGJrI\ pDrHcefUcGJrI

pwifwnfaxmifonfh 1975 rS2006 xd Microsoft ukrÜPD\ xkwfvkyfyHkenf;pepfrSm Gates tay:wGif

vHk;0rlwnfav\/ xkwfukefrsm;\ trsdK;trnfpHkvifa&;udk tifwdkuftm;wdkuf wuf<upGmcsJUxGifazmfaqmifcJhjyD;

aps;uGufudkxda&mufpGm vTrf;rdk;csLyfudkifxm;EkdifcJhonf/ ol\tBuD;wef;refae*smrsm;? y½dk*&rfrefae*smrsm;ESifh

Gates rSefrSefawGUqHkjyD; vkyfief;wGif;&Sd 4if;wdkU\[muGufrsm;? ukrÜPD\a&&SnftusdK;pD;yGm;udk xdcdkufapEdkifaom

tcsufrsm;udkjrifatmif a0zefaxmufjyavh&Sddonf/ wpfckckwifjypOf? oloabmrusvQif Bum;Nzwfí

“Bum;&orQxJrSm? tokH;rusqkH;yJ” oltm;r&aomtcgrsm;wGif “ bmvdkU'DrSmvkyfaeao;vJ? jidrf;csrf;a&;wyfzGJU

[The Peace Corps] oGm;0ifygvm;}} vufatmufi,fom;rsm; vkyfp&m&Sdonfrsm;udk aESmifhaES;cdkuyfvQif ]]at;?

½Hk;ydwf&ufusawmh igyJvkyfvdkufr,f}} ponfjzifhaighhajymwwfavonf/ Microsoft wGif Gates &SdcJhaom

ordkif;wavQmuf ol\tcef;u@rSmt"dutm;jzifh pDrHcefUcGJtkyfcsLyf&efESifh tvkyftrIaqmifu@

rsm;jzpfcJhonf/ tapmydkif;ESpfrsm;wGif software a&;oltjzpfwuf<upGmvkyfcJhonf/ twHkUtvSnfhtm;jzifh ukrÜPD\

Programming Language xkwfukefrsm;jzpfaom TRGS-80 Model 100 udkpwifvkyfudkifpOfuwnf;u?

,if;wdkU\ Coade rsm;udk ol0ifa&;cJhonfrSm 1989-ckESpftxdaemufqHk;jzpf\/

Bill ates(bDvf*dwf(pf)) tydkif; (2)(bDvf*dwf(pf)) tydkif; (2)

(bDvf*dwf(pf)) tydkif; (2)(bDvf*dwf(pf)) tydkif; (2)(bDvf*dwf(pf)) tydkif; (2)

William Henry Gates III [KBE]

44

KNOWLEDGE


atesw&m;&ifqdkifjcif;w&m;&ifqdkifjcif;

w&m;&ifqdkifjcif;w&m;&ifqdkifjcif;w&m;&ifqdkifjcif;

Gates \cGifhjyKcsufjzifh qHk;jzwftaumiftxnfazmfaom ukrÜPD\vkyfaqmifrItrsm;pkrSm? &Hk;wGif Gates

udk,fwdkifac:tppfcH&onftxdjzpf\/ ,if;rSm tar&dueftpdk;&ESifh Microsoft trI 1998 jzpf\/ ,if;udk

wufa&mufem;axmifaom owif;orm;rsm;u tppfcH&mwGif Gates onf ppfar;csufrsm;udk ra&r&mvSD;vTJ

ajzBum;cJhonf[k xGufqdkBuonf/ ppfaq;a&;rSK; a';ApfbdGKiftuf(pf) [David Boies ] udkvnf; jyefvSefjiif;cHkcJhjyD;

em;vnf&efcufcJaom xGufqdkcsufrsm;udk ay;cJhonf/ ar;orQudk “uRefawmfrrSwfrdawmhyg”/ qdkaom

pum;rsm;udk Budrfzefrsm;pGmajzBum;cJh&m w&m;olBuD;udk,fwdkif x&,f&onftxdjzpf\/ tqdk;qHk;rSm Microsoft

\ enf;ynmwm0efcH Gates \ jiif;qdkcsufrsm;rSm rrSefuefaBumif; tpdk;&a&SUaersm;u Gates tm;

tjyeftvSefydkUcJhonf E-mail rsm;udkwifjyí twdtvif;azmfxkwfEdkifcJhjcif; jzpf\/ rnfodkUjiif;qdkumrI w&m;&Hk;u

Gates udk&Sm;rif;Oya' [Sherman Antitrust Act] udkcsdK;azmufí vuf0g;BuD;tkyfrI? aps;uGufudkcsKyfudkif&efBudK;pm;rI

vkyfief;wlrsm;udk rjydKifqdkifEdkifatmif ydwfqdkUrIwdkUjzifh tjypf&SdaBumif; pD&ifcsufcscJhonf/

Microsoft rS tem;,lcJhcsdefrSpí? y&[dwvkyfief;rsm; ESifh tjcm; [Project] rsm;udkqufvkyfonf/

½kyfydkif;qdkif&m Oya'\oGifjyifvu©Pm [The Creater of

Physical Laws ] acgif;pOfjzif h ]aumfeJvf} [cornell]

wuúodkvfwGif ]&pfcswfaz;rJef;}\ ydkUcscsufrsm;udk BBC

rSzrf;,lxm;onfh Massenger Lectures Series twGJrsm;\

AD'D,dkrlydkifcGifhudk Gates 0,f,lcJhonf/ 4if;wdkUudk rnfolrqdk

Microsoft’s Project Tuva wGif OnLinerS0ifBunfhEkdif\/ 2010

{jyDvwGif rufqmcsL;qufenf;ynmodyÜH [Mussachusetts

Instituate of Technology] rSvma&mufa[majym&ef

zdwfBum;cH&avonf/ Gates u tem*gwfurÇm

45

KNOWLEDGE


jyóemrsm;udk 0dkif;0ef;ajz&Sif;MuzdkU ausmif;om;rsm;udk ajymBum;cJhonf/

ates ESifh rdom;pk ESifh rdom;pk

ESifh rdom;pk ESifh rdom;pk ESifh rdom;pk

Zefe0g&Dv 1&uf 1994 wGif wufqufjynfe,f ]'Jvm(pf)} [Dallas] rS ]r,fvif'gz&Jef(pf)}

[Melinda French] ESifh vufxyfonf/ uav;3a,mufrSm 1996ckESpfarG; orD; ]*sJefezguufo&if;*dwf} [Jen

nifer Katharine Gates] ? 2002 arG; orD; ]zDbD tuf'JvD*dwf} [Phoebe Adele Gates ] ? om; ]a&mf*sDa*smef*dwf}

[Rory John Gates ] 1999arG; wdkUjzpf\/ Gates rdom;pkaetdrfrSm? awmifukef;apmif;wGif;&Sdí ajrom;jzifh

um&Hxm;jyD; tdrfrSma&uefay:rdk;vsuf&Sd\/

2006 ckESpf uif;aumifwDvlxkrSwfwrf; [King County Public records ] t& ajrESifhtdrf 2&yfaygif; a':vm

125oef;wefzdk;&SdjyD; ESpfpOfajr,mydkifqdkifrI tcGefxJuyif 991.000 a':vmjzpf\/ pwk&ef;ay 66.000

us,faomtdrfESifh? a&atmufwGif aw;*Dwrsm;em;qifEkdif onfh ay60euf a&ul;ueftjyif pwk&ef;ay 2500&Sd

tm;upm;½HkwdkUygonf/ xrif;pm;cef;yifvQif ay 1000 pwk&ef;ywfvnf&Sdavonf/ wjcm; Gates

pkaqmif;ypönf;rsm;wGif udk'ufvufpwm [Codex Leicester] ac: vD,dkem'dk'gAifcsD Leonardo da Vivci] \

46

KNOWLEDGE


a&S;a[mif;vufa&;rl odyÜH*sme,f [Sciennfic, ournal] 30vJygonf/ 4if;wdkUrSm 1994 ckESpf uav;vHyGJwpfckwGif

a':vm 30.8 oef;jzifh 0,f,lcJhjcif;jzonf\/ ,if;trnfrSm vufpwmjrdKUpm; [Earl of Leicester] ? aomrwfukwf

[Thomas Coke] u 1717 ckESpfwGif 0,f,lcJhí Codex rSm a&S;a[mif;vufa&;rlusrf;pm [kt"dyÜg,f&onf/

Gates rSm pmvJtvGefzwfoljzpf\/ oltdrf\ pmBunfhwdkufBuD;\ rsufESmBuufrSmtar&duefpma&;q&mBuD;

tuf(zf)paumzpf*sme,f [F. Scott. Fit Gerald] \ 1925wGif xkwfa0aom o,f*&dwf*uf(pf)bD 0wÅLxJrS

pum;tudk;tum;rsm;udk a&;oGif;xm;\/ Gates\tjcm;0goemrsm;rSm b&pf(csf)zJ [bridge] upm;jcif;? tennis

ESifh golf wdkUjzpf\/

csdK;azmufaom csdK;azmufaom

csdK;azmufaom csdK;azmufaom csdK;azmufaom ates

,OfpD;urf;,OfpD;urf;

,OfpD;urf;,OfpD;urf;,OfpD;urf;

1997-'DZifbm 13rSm? um;tjrefarmif;rI? Stop Sign udkr&yfbJqufarmif;rI? vdkifpifrJharmif;rIrsm;jzifh

e,l;ruúqDudkwGif Gates tzrf;cH&onf/ ,cifuvJESpfBudrf tzrf;cH&zl;jyDjzpf\/ ,if;wdkUudk Gates trSwf

&qJjzpfonf/

ates ESifhy&[dw ESifhy&[dw

ESifhy&[dw ESifhy&[dw ESifhy&[dw

tvGefcsrf;omvmaomoludk vSLapwef;apcsifaom vltrsm;\qE´udk Gates odvmonf/

tar&duefpufrIvkyfief;&SifolaX;BuD; a*smefa';Apfqifa&muDzJvm; [John Davison Rockefeller] ESifh

paumhvlrsdK;tar&duef oHrPdvkyfief; olaX;BuD; tJef'½l;umae*D [Andrew Carnegie] wdkUvkyfcJhonfrsm;udk

Gates avhvmonf/ ESpfOD;pvHk;rSm trsm;tusdK;aqmiftvSL&Sif

olaX;BuD;rsm; [Philanthropist] jzpfonf/ 1994 wGif Microsoft rS

ol\ Stock tcsdKUudka&mif;csí Willion H. Gates Foundation

udkxlaxmifonf/ 2000ckESpfa&mufaomf ZeD;jzpfolESifhtwl? rdom;pk

Foundation oHk;ckudkwpfpkwpnf;xJaygif;í EGrf;yg;olrsm;

udkaxmufyHulnD&ef Bill & Melinda Gates Foundation [lí

txift&Sm;aqmif&GufvSL'gef;aom urÇmtBuD;qHk; y&[dw

Foundation BuD;jzpfvmav\/

tpdk;&ESifh tjcm;tzGJUtpnf;rsm;rS vspfvsL½Ixm;aom urÇmjyóemrsm;udk t"duxm;vkyfaqmif

Buonf/ 2007ckESpfa&mufaomf Bill ESifh Melinda wdkUrSm tar&duefwGif 'kwd,t&ufa&mqHk;vSL'gef;aom

Philanthropisits rsm;jzpfjyD; pkpkaygif; 28-bDvsHa':vmausmfavonf/ yxrrSm Intel Crop

udkxlaxmifcJ hol]a*:'ifarm} [Gordon more] jzpf\/ Gates rSmolcsrf;omorQ\ w0ufausmf

udkvSL'gef;&efuwdjyKxm;onf/ ol\&ufa&mrIrsm;rSm billon thropist [lí ac:qdk&onftxd jzpf\/

47

KNOWLEDGE


todtrSwf jyKcH&jcif;rsm;todtrSwf jyKcH&jcif;rsm;

todtrSwf jyKcH&jcif;rsm;todtrSwf jyKcH&jcif;rsm;todtrSwf jyKcH&jcif;rsm;

1987 ckESpf? ol\32ESpfajrmufarG;aeUrwdkifrSD &ufteJi,ftvdkwGif urÇmausmf azg(bf) [Forbers]

r*¾Zif;BuD;\ tar&dueftcsrf;omqHk;rsm;pm&if;wGif Gatesudkvlod&SifBum;azmfjycJh\/ urÇmtoufti,fqHk;

udk,ftm;udk,fudk; bDvsHemjzpfvmaom xdktcsdefuol\<u,f0rIrSm a':vm 1.25 bDvsHjzpfavonf/

þodkUazmfjycH&jyD; aemufESpfwGifyif Gates \"erSm aemufxyfa':vmoef;900xyfwdk;oGm;cJh\/

wkdif;r*¾Zif;[Times] BuD;uvJ 20&mpk\ BoZmwduúrBuD;rm;aomyk*¾dKvf 100pm&if;ESifh 2004? 2005? 2006

ckESpfrsm;\ BoZm&Sdaom 100pm&if;wGifxJoGif;azmfjycJh\/ usefonfwdkUrSm Gates, Melinda, ESifh U2 tqdkawmf

Bono wdkUudk olwdkU\vlom;jcif;pmemaxmufxm;aom vkyf&yfrsm;aBumifh Times r*¾Zif;uyifa&G;cs,foam

2005-ckESpf\ yk*¾dKvfxl;rsm;tjzpfvnf;aumif;? 2006ckESpfwGif uREkfyfwdkUacwf\ol&Jaumif;rsm; [Heroes

of our time] pm&if;wGif eHygwf 8-

1999 Sunday Time ( CEO of the year )

1998 Time ( Top 50 Cyber Elite )

2001 The Guardian( Upside Elite 100 , No 2 )

1994 jAdwdoQuGefysLwmtoif;\ 20a,mufajrmuf xif½Sm;aomtzGJU0if

2000 Nyenrode Business Universiteit, e,fomvefEdkifiHrS ay;tyfaom*kPfxl;aqmif yg&*lbGJU

( Honrary Doctorates ) atmufygwuúodkvfrsm;\ *kPfxl;aqmifbGJUrsm;

2002 The Royal Institude of Technology, Stockholm, Sweden

2005 Waseda University, Tokyo, Japan

2007 Tsinghua University, Belijing, China

2007 Harvard University

2008 Karolinska Instituted, Stockholm, Sweden

2007 Cambridge University , England

48

KNOWLEDGE


2007 Be---- University rS cefUtyfaom *kPfxl;aqmiftzGJU0ifvlMuD;

2005 Queen Elizabeth II bk&ifrMuD;rS tyfESif;aom KBE bGJU

( An Honorary Knight Commander of the Order of he British Empire )

xyfavmif;í ydk;rTm;bmom&yfqdkif&m trnftjzpf ydk;rTm;rsdK;pdwfwpfckudk Bill Gates Flower Fly [k *kPfjyK

rJSUac:cJhonf/ 2006 Edk0ifbmvwGif Gates ESifhh ZeD; Melinda udk urÇmwpf0Srf;ESifh txl;ojzifhruúqDudkEdkifiHwGif

usef;rma&; ynma&;e,fy,fwdkUrSm 4if;wdkUaqmif½GufcJhaom vlrIa&;vkyfief;rsm;twGuf tuf(Zf)wuftD;*Jvf

( The Order of the Aztec Eagle ) udk csD;jr§ifhjcif;? 2009 atmufwkdbmwGif Gates \pD;yGm;a&;vkyfief;ESifh

vlom;tusdK;jyKvkyfief;rsm;wGif atmifjrifrIrsm;twGuf 2010 Bower Award for Business Leadership of the

Franklin Institute qkudk ay;tyfrSmjzpfaMumif; aMujimcJhjcif;? ol\aqmif½GufrIrsm;tay: tar&duef

vli,fuif;axmuftzGJU ( Boy Sconts of Amarica ) \ tjrifhqHk;aiGuRJqk ( The Silver Buffalo ) rsm;tygt0if

urÇmwpf0Srf;wGif *kPfjyKtodtrSwfjyKjcif;rsm; cHcJh&onf/Aztec rSm ruúqDudkwGif 12&mpkutajccHtifyg,m

wnfaxmifcJhaom e[l0gwDvf ( Nahuatl ) rsdK;EG,f0if tif'D;,ef;rsm; jzpf\/ argefwDZl;rm;bk&if\( Montezuma)

4if;tifyg,mrSm 1512wGif pydefvlrsdK;e,fajropf½Smol pmeefaumfwuf ( Hernan Cortes ) \

usL;ausmfacsrIef;odrf;ydkufjcif;cH&onf/

1997 ckESpfwGif Gates \ MuG,f0rIrSm bDvsH 101 a':vmausmfoGm;cJh&m owif;pmrsm;rSbDvsH&mcsDí

csrf;omol ( Centibillionaire ) [k wyfíac:&efjzpfcJh\/ odkUaomf dot com pD;yGm;a&;ylaygif;MuD; aygufuGJusqHk;csdef

2000 aemufydkif;wGif ol\ Microsoft holdings \ Stock wefzdk;rsm; usqif;cJhonf/

odkUwkdifol\MuG,f0rI 0ifaiGESifh ywfoufí? ]] tu,fívrf;oGm;&if; Gates om a':vm 1000

vufuusoGm;&ifbmvkyfrvJ [laomtar;udk jyefaumufrSmr[kwfbl; bmjzpfvdkUvJqdkawmh

aemuf1puúefUtwGif;rSmyif xdk1000udk ol\0ifaiGrSm jyef&oGm;jyDjzpfaomaMumifh[k ajzxm;onf/

Gates udk,fwdkif ? ]]uRefawmhfrSm a':vmbDvsH 100 ½Sdw,f? bmrSrvkyfyJwpfaeUudk a':vm3oef;EIef;eJU

vmrJhESpf 100 txdoHk;vdkU&w,f}} [kajymcJhzl;onf/ wpfMudrfwGifvnf; ]] vlwdkif; E mail oHk;aewJhacwfrSm

uRefawmfh Inbox xJudkvJ Spam mail awG tjrJ0ifaewmygyJ/ 'DpmawGxJrSm uRefawmfhudktaMuG;oHo&mxJu

vGwfatmif ulnDr,fvdkUurf;vSrf;wJhpm? b,fvdkvsifvsifjrefjref MuD;yGm;csrf;omatmif vkyf&r,fqdkwJhpmrsdK;awG

vnf;ygoAs}} [k Gates uajymonf/ MudMudwufMuG,f0rI\ aemufuygvmaom tm½HkpdkufcHae&jcif;udk

rESpfoufaom Gates u urÇmtcsrf;omqHk;vl rjzpfcJh&ifaumif;om;[k olUqEudkxkwfazmfzl;onf/

49

KNOWLEDGE


Microsoft tjyif ol\tjcm;&if;ESD;jr§KyfESHrIrsm;rS 2006 wGifvpmtaejzifh a':vm 616, 667, ESifh qkaMu;aiG

a':vm 350, 000 pkpkaygif;a':vm 966, 667 &cJhonf/ ,if;wdkUrSm aumfbpf ( Corbis digital imaging Co.,)

? ESpfumvMumjrifhpGm cifrifvmaomoli,fcsif; 0g&if;bwfzwf( Warren Buffet ) OD;pD;aom buf½Sm;[ufoa0;

&if;ESD;jr§KyfESHrIukrÜPD ( Berkshire Hathaway Investment Co.,) wGifvJ Gates rSm'g½dkufwmjzpf\/odkUwdkif 2010

rwfvwGif Gates rSm 'kwd,urÇmhtcsrf;omqHk;odkU avsmhuscJhonf/ vuf½SdurÇmh tcsrf;omqHk;rSm vufbEGefvlrsdK;

ruúqDudkEdkifiHom; bDvsHemumvdk(pf)qvif; ( Carlos Slim ) jzpf\f/

&if;ESD;jr§KyfESHrIrsm;&if;ESD;jr§KyfESHrIrsm;

&if;ESD;jr§KyfESHrIrsm;&if;ESD;jr§KyfESHrIrsm;&if;ESD;jr§KyfESHrIrsm;

- Cascade Investments LLC, yk*¾vdu&if;ESD;jr§KyfESHrI OD;ydkifukrÜPD Kirkland City, USA ( Gates pDrHtkyfcsKyfonf

- Log C3 - think-tank Co., ( Gates wnfaxmifonf )

- Corbis - digital Image Licensing & rights services Co.,

- TerraPower - nuclear reactor design Co.,

a&;cJhaom pmtkyfrsm;a&;cJhaom pmtkyfrsm;

a&;cJhaom pmtkyfrsm;a&;cJhaom pmtkyfrsm;a&;cJhaom pmtkyfrsm;

- The Road Ahead ( 1995 )

- Business & The Speed of Though ( 1999 )

The E d

50


KNOWLEDGE

A1�

ter atio al rospe tus E-book tydkif; ( 2 ) tydkif; ( 2 )

tydkif; ( 2 ) tydkif; ( 2 ) tydkif; ( 2 )

51


KNOWLEDGE

A1�

52


KNOWLEDGE

A1�

53


KNOWLEDGE

A1�

54


KNOWLEDGE

A1�

55


KNOWLEDGE

A1�

56


KNOWLEDGE

A1�

57


KNOWLEDGE

A1�

58


KNOWLEDGE

A1�

59


KNOWLEDGE

A1�

60


KNOWLEDGE

A1�

61


KNOWLEDGE

A1�

62


KNOWLEDGE

A1�

63


KNOWLEDGE

A1�

64


KNOWLEDGE

A1�

65


KNOWLEDGE

A1�

66


KNOWLEDGE

A1�

67


KNOWLEDGE

A1�

68


KNOWLEDGE

A1�

69


KNOWLEDGE

A1�

70


KNOWLEDGE

A1�

71


KNOWLEDGE

A1�

72


KNOWLEDGE

A1�

73


KNOWLEDGE

A1�

74


KNOWLEDGE

A1�

75


KNOWLEDGE

A1�

76


KNOWLEDGE

A1�

77


KNOWLEDGE

A1�

78


KNOWLEDGE

A1�

79


KNOWLEDGE

A1�

80


KNOWLEDGE

A1�

Next issuewGifqufvufzwf½Iyg&efwGifqufvufzwf½Iyg&ef

wGifqufvufzwf½Iyg&efwGifqufvufzwf½Iyg&efwGifqufvufzwf½Iyg&ef

81

KNOWLEDGE


Taking�Charge�of�ProcrastinationReason: Unpleasant Task:

– ake up our o re ards

– sk so eo e else to he k up o ou

– de ti the u pleasa t o se ue es o N T doi the task

Reason: Overwhelming Task:

– Break the pro e t i to a set o s aller ore a a eable tasks

– tart ith so e ui k s all tasks i ou a e e i these are t the lo i al irst a tio s

Ti e a a e e t tydkif; ( 4 ) tydkif; ( 4 )

tydkif; ( 4 ) tydkif; ( 4 ) tydkif; ( 4 )

• tep e o i e that ou re ro rasti ati

• tep ork out ou re ro rasti ati

• tep et o er it

82

KNOWLEDGE


ear he to sa N

3/10/2011 40

� ou a t do e er thi

� o t u dertake thi s ou a t o plete

� e ai o siste t to our oals

Taki Char e o ro rasti atio

ather a e his so a bu dle o sti ks a d asked hi to break it ter the bo

stru led the ather took the bu dle u tied it a d broke o e sti k at a ti e

3/10/2011 Page�39

83

KNOWLEDGE


ear he to sa N

hat to a No to

• What will this commitment mean?

• If you had to take on this commitment tomorrow, would it—considering what you’ve planned—be a good use of your time?

o to a No

• Give a reason.

• Be diplomatic.

• Suggest a trade-off.

• Don’t put off your decision.3/10/2011 41

3/10/2011 42

84

KNOWLEDGE


This is the TimeThis is the PlaceThis is your Life

This is your Opportunity

Seize the DayUse this Moment

ACT NOW3/10/2011 43THE END

85

T Co puter Trai i Certerarti al

'Doifwef;rSmawmh uGefysLwmwpfvHk;&JU tajccHenf;ynmrsm; jzpfaom Hardware ydkif;ESifh Softwareydkif; ESpfydkif;wdkYudk t"duxm;í oifMum;jyoay;oGm;rSm jzpfygw,f/ ,ckoifwef;rSmawmh Hardware ydkif;u@rSm uGefysLwmtwGif;yg0ifaom Hardware ydkif;rsm;udk Install jyKvkyfjcif;? Trouble shoot jyKvkyfjcif;ESifh?

Maintance jyKvkyfjcif; tp&Sdaomtao;pdwf Hardware ydkif; enf;ynmrsm;udk oifMum;jyoay;rSmjzpfygw,f/

'ghtjyif Software enf;ynmtwGif;ü rdrdtoHk;jyKaeaom Operating System rStp Software Toolsrsm;onhfwdkifatmif rnfodkUrnfyHktoHk;csEdkifovJqdkwmudk Theory rStp? Practical tqHk; teD;uyf

oifMum;rIpepfyHkpHjzifh oifMum;ay;oGm;rSm jzpfygw,f/

oifhtaeeJU 'Doifwef;udk wwfajrmufNyD;qHk;oGm;ygu Computer System Technician tjzpf

vkyfief;cGifrSmaumif;rGefpGm wm0ef,lEdkifrSmjzpfygw,f/

Net ork E i eeri

,ckoifwef;rSmawmh Computer Network csdwfqufwJh enf;ynmrsm;udk tav;ay;oifMum;jyoay;

oGm;rSmjzpfygw,f/ 'Doifwef;rSmawmh Computer Networ k csdwfqufwJh oifcef;pmrsm;tjyif?

tvGefpdwf0ifpm;zG,f aumif;wJh Server ydkif;qdkif&menf;ynmrsm;udkvnf; jznhfpGufíoifMum;jyo

ay;oGm;rSmjzpfygw,f/

oifhtaeeJY ,ckoifwef;udk wwfajrmufNyD;qHk;oGm;ygu Computer Network udk csdwfqufwwf½kH

tjyif Computer Network twGif;ü toHk;jyKwJh Server enf;ynmrsm;udkvnf; Theory ESifh Practical ygwwfajrmufoGm;rSmjzpfygw,f/ oifwef;NyD;qHk;ygu Network Administrator wpfa,muftjzpf cdkifrmwJh

b0 tmrcHcsufudk &&SdoGm;rSmjzpfygw,f/

er er

Microsoft &JU aemufqHk;aom pGrf;aqmif&nfjrifhrm;aom Server 2008 udk Theory ESifh Praticalususaygif;pnf;í oifMum;jyoay;aom Course wpfckjzpfygw,f/ 4if; Course &JU twGif;rSmawmh Server

86

Server 2008 &JU ta&;BuD;aomTheory oifcef;pmrsm;udk pepfwusoifMum;jyoay;jcif;? vdktyfaom

Server 2008 &JU enf;ynmrsm;udk xda&mufpGmoifMum;ay;jcif;wdkUudk jyKvkyfay;xm;ygw,f/ 'ghtjyif

,ck Server 2008 twGif;üyg0ifvmwJh enf;ynmtopfrsm;udkvnf; vufawGUususpDrHxdef;csKyf

Edkifap&eftwGufvnf; Theory ESifh aygif;pyfum Pratical oifcef;pmrsm;udk oifMum;jyoay;oGm;rSm

jzpfygw,f/

oifwef;NyD;qHk;oGm;ygu Microsoft &JU Upgrade Server Version jzpfaom Server 2008 udk oifudk,fwdkif

oDtdk&DESifh vufawGUususaygif;pyfum wwfajrmufoGm;rSmjzpfygw,f/

CCN

Computer Network \tqifhjrifh enf;ynmudk avhvmmoif,lvdkMuaom ynm&Sifwdkif;twGuf

txl;oifh avsmfonhf oifwef;wpfckjzpfygw,f/ 4if; CCNA udk Cisco qdkwJh ukrÜPDrS OD;pD;NyD;

usif;yjyKvkyfay;wmjzpfygw,f/ CCNA Course uawmh Network ESifh ywfoufNyD; tajccH&SdNyD;olrsm;twGuf

txl;oifhavsmfwJh oifwef;wpfck jzpfygw,f/ 'ghtjyif ,ckCCNA Course rSmawmh Cisco &JU

pGrf;aqmif&nfjrifhrm;aom enf;ynmrsm;udk avhvmoif,l&rSmjzpfygw,f/ 'Doifwef;rSmawmh Cisco &JUta&;BuD;aom enf;ynmrsm;jzpfonhf Routing, Switching, Firewall qdkwJh oifcef;pmrsm;udk tav;ay;

oifMum;jyoay;oGm;rSmjzpfygw,f/

oifwef;NyD;qHk;ygu Network &JU tqifhjrif henf;ynm&yfrsm;udk wwfajrmufoGm;½H ktjyif?

b0rSmcdkifrmwJh vpmaumif;wpfckeJY jrifhrm;wJhenf;ynm&Siftjzpf toufarG;0rf;aMumif;jyKEkdifrSmjzpfygw,f/

asteri C

,aeY enf;ynmacwfwGif Operating System twGif;ü yg0ifaomtqifhjrifh enf;ynmrsm;udk jznhfpGuf

oifMum;ay;aom oifwef;wpfck jzpfygonf/ 4if;oifwef;wGif Operation System twGif;üyg0ifaom

Registry, Group Policy, DOS mode with Comman Line, System Tipe and Trick ESifh tjcm;aom O/S udk

Recover jyKvkyfay;Edkifrnfh Utilitires rsm;jzifh tzufzufrS jynhfpHkpGm pepfwusoifMum;ay;aeaom

oifwef;jzpfygonf/ 4if;oifwef;onf Computer Hardware NyD;xm;aom ausmif;om;rsm;twGuf

txl;oifhavsmfaom oifwef;wpfckjzpfygonf/

oifwef;NyD;qHk;ygu oifhtaeESifh Microsoft &JU O/S taMumif;rsm;udk Theory tjyif Pratical yg uRrf;usif

wwfajrmufoGm;ygvdrfhr,f/

usi rodu tio

'Doifwef;uawmh *DwudkcHkrifolwdkif; wufxm;oifhwJh oifwef;wpfckjzpfygw,f/ ,aeYacwf

vli,frsm; txl;pdwf0ifpm;aewJh oifwef;wpfckyJjzpfygw,f/ Music Production oifwef;rSmawmh Fl Stu-dio 9, Wavelab 5.0, Acid \ tp&SdwJh Software awGudk toHk;jyKoifMum;ay;rSm jzpfygw,f/ 'ghtjyif Mixing/Mixdown jyKvkyfjcif;? Recording jyKvkyfjcif;rsm;udk 16 Tracks Mixer, Keyboard, Electric Guitar pwJh

wl&d,mrsm;eJU wGJzufoifMum;ay;vsuf &Sdygw,f/

'Doifwef;NyD;qH k;ygu ½kyfoHavmutwGuf vdktyfaewJh*DwoHpOfrsm;pGmud k ud k,fwd kif

zefwD;EdkifrSmjzpfygw,f/

88


KNOWLEDGE

A1�

e elop our eadership kills(tydkif; 1)(tydkif; 1)

(tydkif; 1)(tydkif; 1)(tydkif; 1)

89


KNOWLEDGE

A1�

90


KNOWLEDGE

A1�

91


KNOWLEDGE

A1�

92


KNOWLEDGE

A1�

93


KNOWLEDGE

A1�

94


KNOWLEDGE

A1�

95


KNOWLEDGE

A1�

96


KNOWLEDGE

A1�

97


KNOWLEDGE

A1�

98


KNOWLEDGE

A1�

99


KNOWLEDGE

A1�

100


KNOWLEDGE

A1�

101


KNOWLEDGE

A1�

102


KNOWLEDGE

A1�

103


KNOWLEDGE

A1�

104


KNOWLEDGE

A1�

105


KNOWLEDGE

A1�

106


KNOWLEDGE

A1�

107


KNOWLEDGE

A1�

108


KNOWLEDGE

A1�

109


KNOWLEDGE

A1�

110


KNOWLEDGE

A1�

111


KNOWLEDGE

A1�

112


KNOWLEDGE

A1�

113


KNOWLEDGE

A1�

114


KNOWLEDGE

A1�

115


KNOWLEDGE

A1�

116


KNOWLEDGE

A1�

117


KNOWLEDGE

A1�

118


KNOWLEDGE

A1�

Next issue wGifqufvufzwf½Iyg&efwGifqufvufzwf½Iyg&ef

wGifqufvufzwf½Iyg&efwGifqufvufzwf½Iyg&efwGifqufvufzwf½Iyg&ef

119

��

� ��

��

��

��

�� !� "##$�%&!� "##$�%&!� "##$�%&!� "##$�%&!� "##$�%&'''''(((((�)*� �*� �'*�)*� �*� �'*�)*� �*� �'*�)*� �*� �'*�)*� �*� �'*(((((�� !� � !� �+� �!� � !� �+� �!� � !� �+� �!� � !� �+� �!� � !� �+� �#�%&#�%&#�%&#�%&#�%&'''''(((((

� � � � � � �� #"�#"�#"�#"�#"��,�)� �*� �,��,')�,�)� �*� �,��,')�,�)� �*� �,��,')�,�)� �*� �,��,')�,�)� �*� �,��,')$�%')�$�%')�$�%')�$�%')�$�%')��

�'*��'*��'*��'*��'*�(� � �(� � �(� � �(� � �(� � �� "�� "�� "�� "�� "�&&&&&�� -�� -�� -�� -�� -#)+�)�+' '"�)� ��.��#)+�)�+' '"�)� ��.��#)+�)�+' '"�)� ��.��#)+�)�+' '"�)� ��.��#)+�)�+' '"�)� ��.��#)#)#)#)#)/��/��/��/��/��' /' /' /' /' /��*��*��*��*��*

�� !�� !�� !�� !�� !��

��

��

Business Quotation 120

��

�� !� �� !� �� !� �� !� �� !� ��

��

��'/� "�+�*��'/� "�+�*��'/� "�+�*��'/� "�+�*��'/� "�+�*�'/'/'/'/'/�*� *�+%&�*� *�+%&�*� *�+%&�*� *�+%&�*� *�+%&(�+�' *� ((�+�' *� ((�+�' *� ((�+�' *� ((�+�' *� (#0� ')#0� ')#0� ')#0� ')#0� ')�� &�� &�� &�� &�� &�') � "� -�') � "� -�') � "� -�') � "� -�') � "� -'*�'*�'*�'*�'*��)� ��)� ��)� ��)� ��)� �

��*��*��*��*��*�# ��"#)# ��"#)# ��"#)# ��"#)# ��"#)�� 0��0��0��0��0��11111��' '"�+� ��' '"�+� ��' '"�+� ��' '"�+� ��' '"�+� �

��,�� &�� ,�� &�� ,�� &�� ,�� &�� ,�� &�'$�'$�'$�'$�'$�)*�.�)*�.�)*�.�)*�.�)*�.,#�.,#�.,#�.,#�.,#�.#)#)#)#)#)/�+#*�� --/�+#*�� --/�+#*�� --/�+#*�� --/�+#*�� --�2��2��2��2��2��11111��&&&&&(((((�� *��+*� �� *��+*� �� *��+*� �� *��+*� �� *��+*� �#�+��#�+��#�+��#�+��#�+��

�1�) �1�) �1�) �1�) �1�) *'*'*'*'*'(� !��(� !��(� !��(� !��(� !��!�� $� �,'�!*� #��3�2'0*�� ,�!�� $� �,'�!*� #��3�2'0*�� ,�!�� $� �,'�!*� #��3�2'0*�� ,�!�� $� �,'�!*� #��3�2'0*�� ,�!�� $� �,'�!*� #��3�2'0*�� ,�(�,'(�,'(�,'(�,'(�,'11111�� )))))'� �$� �,�+4'� �$� �,�+4'� �$� �,�+4'� �$� �,�+4'� �$� �,�+4

*�*�*�*�*�&&&&&11111�*� #�� *� #�� *� #�� *� #�� *� #�� #� *'#� *'#� *'#� *'#� *'(� !� ��(� !� ��(� !� ��(� !� ��(� !� ��!� �,�!� �,�!� �,�!� �,�!� �,�(� $(� $(� $(� $(� $# # # # # !�� ,� /� !��!�� ,� /� !��!�� ,� /� !��!�� ,� /� !��!�� ,� /� !��!� �,�!� �,�!� �,�!� �,�!� �,�(� �,� /� !��(� �,� /� !��(� �,� /� !��(� �,� /� !��(� �,� /� !��!5!5!5!5!5

�,��,��,��,��,�(� �,� /� !�(� �,� /� !�(� �,� /� !�(� �,� /� !�(� �,� /� !��'+�'+�'+�'+�'+!�� ,�!�� ,�!�� ,�!�� ,�!�� ,�(� � 0 $(� � 0 $(� � 0 $(� � 0 $(� � 0 $�)*��)*��)*��)*��)*�' $� �,��)� 6#3� �' $� �,��)� 6#3� �' $� �,��)� 6#3� �' $� �,��)� 6#3� �' $� �,��)� 6#3� �#� 3�� #� 3�� #� 3�� #� 3�� #� 3�� #�+'/#�+'/#�+'/#�+'/#�+'/��

�,�� '+� -0 2��# �� ,�(�,�� '+� -0 2��# �� ,�(�,�� '+� -0 2��# �� ,�(�,�� '+� -0 2��# �� ,�(�,�� '+� -0 2��# �� ,�( '22�%'22�%'22�%'22�%'22�%�� )�� )�� )�� )�� )�*%# *�3�&�*%# *�3�&�*%# *�3�&�*%# *�3�&�*%# *�3�&��(� ' $�$(�' $�$(�' $�$(�' $�$(�' $�$# # # # # !�� *�$!�� *�$!�� *�$!�� *�$!�� *�$�4�4�4�4�4

*�*�*�*�*��%� ��%� ��%� ��%� ��%� �� 30�� !.�� 30�� !.�� 30�� !.�� 30�� !.�� 30�� !.��!� "�!� "�!� "�!� "�!� "��*� �,�� 2)*� �,�� 2)*� �,�� 2)*� �,�� 2)*� �,�� 2)�$�$�$�$�$�� ,�*� �*�.�� ,�*� �*�.�� ,�*� �*�.�� ,�*� �*�.�� ,�*� �*�.,'�� 2),'�� 2),'�� 2),'�� 2),'�� 2)�'��'��'��'��'��*� ��*� ��*� ��*� ��*� �)0*�)0*�)0*�)0*�)0*��

.....,'�� '3&,'�� '3&,'�� '3&,'�� '3&,'�� '3&�*� (�*� (�*� (�*� (�*� (#0� �#0� �#0� �#0� �#0� �#� "�#� "�#� "�#� "�#� "�� ,�� ,�� ,�� ,�� ,�� '*/�$'*/�$'*/�$'*/�$'*/�$# ��# ��# ��# ��# ��

��)� ��)� ��)� ��)� ��)� �� )02/�� )02/�� )02/�� )02/�� )02/�)�)�)�)�)��' '"� "� �,��# %)��' '"� "� �,��# %)��' '"� "� �,��# %)��' '"� "� �,��# %)��' '"� "� �,��# %)#-�� )#-�� )#-�� )#-�� )#-�� )"""""' �7'�' �7'�' �7'�' �7'�' �7'��# �# �# �# �#

��8��8��8��8��8��

��

121

��8��8��8��8��8�� 8��8��8��8��8�

� � � � � � � � ��

�� #0� ')#0� ')#0� ')#0� ')#0� ')�� 0 �90�5� (�� 0 �90�5� (�� 0 �90�5� (�� 0 �90�5� (�� 0 �90�5� (#0�,'#0�,'#0�,'#0�,'#0�,'11111�� *%�2�-�2� */�&&�� *%�2�-�2� */�&&�� *%�2�-�2� */�&&�� *%�2�-�2� */�&&�� *%�2�-�2� */�&&*� �,'�� '$*� �,'�� '$*� �,'�� '$*� �,'�� '$*� �,'�� '$$$$$$)))))�**�**�**�**�**

(((((#0)� #)#0)� #)#0)� #)#0)� #)#0)� #)"""""' �7'�' �7'�' �7'�' �7'�' �7'��# �# �# �# �# !*� %�)!*� %�)!*� %�)!*� %�)!*� %�)2��2��2��2��2��11111�$� ��$�$� ��$�$� ��$�$� ��$�$� ��$*� ' $� '�*� ' $� '�*� ' $� '�*� ' $� '�*� ' $� '��)))))'2�'2�'2�'2�'2��$� ��$� ��$� ��$� ��$� ��*� &*� &*� &*� &*� &�'$�'$�'$�'$�'$44444

�)*� �#�)*� �#�)*� �#�)*� �#�)*� �# ,�),�),�),�),�)��(��(��(��(��(#0��#0��#0��#0��#0��#0�'&#0�'&#0�'&#0�'&#0�'&*#�,'*#�,'*#�,'*#�,'*#�,'11111��'� *��'� *��'� *��'� *��'� *�(((((&&&&&��#-�.��#-�.��#-�.��#-�.��#-�.#)#)#)#)#)/� "�.��,�#�,4/� "�.��,�#�,4/� "�.��,�#�,4/� "�.��,�#�,4/� "�.��,�#�,4

�)*� �,'��+#*�� &�)*� �,'��+#*�� &�)*� �,'��+#*�� &�)*� �,'��+#*�� &�)*� �,'��+#*�� &�/�/�/�/�/��&&&&&(� � ,'*� 3�2#+��' � �*�(� � ,'*� 3�2#+��' � �*�(� � ,'*� 3�2#+��' � �*�(� � ,'*� 3�2#+��' � �*�(� � ,'*� 3�2#+��' � �*�'3&'3&'3&'3&'3&�*,�$�%'��*,�$�%'��*,�$�%'��*,�$�%'��*,�$�%'��) � � �) � � �) � � �) � � �) � �

(((((#0)� &#0)� &#0)� &#0)� &#0)� &�-�-�-�-�-�� #� �,�� #� �,�� #� �,�� #� �,�� #� �,�� :�:�:�:�:�� ,'�� (� �� ,'�� (� �� ,'�� (� �� ,'�� (� �� ,'�� (#0)� *�#0)� *�#0)� *�#0)� *�#0)� *�(((((&&&&&�� #-� $�� #-� $�� #-� $�� #-� $�� #-� $#� "� �,� "*� -0&#� "� �,� "*� -0&#� "� �,� "*� -0&#� "� �,� "*� -0&#� "� �,� "*� -0&44444

-�&-�&-�&-�&-�&*� �,�� $*� �,�� $*� �,�� $*� �,�� $*� �,�� $*�#-� �,��#)*�#-� �,��#)*�#-� �,��#)*�#-� �,��#)*�#-� �,��#)"""""' �7'�' �7'�' �7'�' �7'�' �7'��# �' $� ��# �' $� ��# �' $� ��# �' $� ��# �' $� ��*�+�+3�)*�� (*�+�+3�)*�� (*�+�+3�)*�� (*�+�+3�)*�� (*�+�+3�)*�� (#0�.�&&#0�.�&&#0�.�&&#0�.�&&#0�.�&&

3�� *022�**-0&3�� *022�**-0&3�� *022�**-0&3�� *022�**-0&3�� *022�**-0&��

� �� %,' � ��%,' � ��%,' � ��%,' � ��%,' � ��'-'-'-'-'-�� )�� )�� )�� )�� )�*�$�*�$�*�$�*�$�*�$� �� '%'%'%'%'%%#)�� #++0 �2'�%#)�� #++0 �2'�%#)�� #++0 �2'�%#)�� #++0 �2'�%#)�� #++0 �2'��# *�# *�# *�# *�# *

��

� � � � � �� 8��8��8��8��8��

��

� �� ; �� ; �� ; �� ; �� ; ��

122

��

��

�� 8� �� 8� �� 8� �� 8� �� 8� ��

�� !�� !�� !�� !�� !��

�� 8� �� 8� �� 8� �� 8� �� 8� �� ;�� ;�� ;�� ;�� ;��

��

��

��

� �� 8��8��8��8��8��

��

��

�� 8� �� 8� �� 8� �� 8� �� 8� ��

��

� �� 4� �� 4� �� 4� �� 4� �� 4� ��

123

KNOWLEDGE


ro e t a a e e t Che klistA project management checklist is an essential tool to “quick start” any project. Whether you’re starting a projectfrom the beginning, or you’re taking over one that’s already been started, you’ll need to check that everythingyou need is in place. The questions in this project management checklist are designed to help you manageyour project successfully.

The first step is to ensure you follow a clear project management model, which will help you identify clear aims,objectives and desired outcomes. Follow our project management guidelines to help you get started. The nextthing you’ll need to do is to check that you have everything in place. That’s where our project managementchecklist will help.

ro e t a a e e t Che klist

The checklist is a useful questioning technique, adapted from our problem solving

activity. It’s based on the principle of asking the right questions in the right way - a structured way. Questions areperhaps the best management tools at your disposal.

This project management checklist uses the “pecking order” of questions with the more powerful questions(why, how and what) asked first, followed by the less, but perhaps more specific questions (who, where andwhen)

The table below shows the structure of the question checklist, and includes some examples of moredetailed, follow-up questions. It’s easy to develop a checklist to suit your own situation but don’t just use thequestion checklist for problem solving. You could also use it for routine situation analysis or to consider howyou might deal with opportunities.

·

WHATWhat ( exactly ) do I want to achieve ?What really matters to me ?What are the objectives and outcomes of the project ?What issue/problem or opportunity is the project addressing ?What resources have been allocated to the project ?What are the risks involved in the project ?What assumptions do you need to test ?What is the budget for the project ?

124

KNOWLEDGE


HowHow will the situation be different when the project is successfullycompleted ?How do I contribute to the organization ?How will changes to the project be reported and approved ?How was the budget created ?How was the project plan created ( or the timescale for the projectdetermined ) ?How will the situation be different ?How can I find out more ?

WhenWhen does the project start ?When is the planned finish date ?When did the issue arise ?When do we need to act ?By When must it be resolved ?

WhereWhere are the resources for the project ?Where will the project team be based ?Where is the project plan ?Where does is Impact ?Is the "Where" important ?

Who is the sponsor of the project ?Who cares about the project ?Who is on the project team ?Who is affected ?Who is knows most about the situation ?Who needs to be informed ?Who am I trying to please ?

WhoThese questions may be just a starting point and you may wish to personalize the tool by adding your ownspecific questions. The important thing is that you ask the right questions before starting work.

There are numerous, comprehensive project management models in use. Though perhaps some are toocomprehensive!When you’re a busy manager, and managing projects as well, often what’s needed is a clear but simple pro-cess to follow. The 4D model provides such a process. The model is outlined below .

WHYWhy is the organization investing in the project ?Why have I been asked to manage the project ?Why do I want to achieve a solution ?Why didi the problem or apportunity arise ?Why do I need to find a solution or way forward at all ?

125

KNOWLEDGE


The 4 D Model

Managing a project is one of those situations where you have defined responsibility as the project managerto deliver something tangible in the organisation. You can make an impact both on your organisationsperformance and on you own reputation.

So it is vital to set off on the right path. Clarity in the aims, objectives and outcomes for the project are essentialto start well and must be then supported by a well crafted plan.

The project may be something you’ve been given to manage, with little say in its definition. Or it may be some-thing you have generated yourself.Either way, to complete a successful project, and thus be a happy project manager, beginning with properplanning time is critical. The importance of ensuring that outcomes are meaningful and worthwhile before youstart cannot be overemphasized.

127

128

Documents

Digital Learning Kit