Data Warehuse and Telecmmunicatins Industry

Transcription

1 Business Intelligence fr the Telecmmunicatins Industry Definitin Data warehusing is the prcess f integrating enterprise-wide crprate data int a single repsitry. The resulting data warehuse may then supprt a variety f decisin analysis functins as well as strategic peratinal functins. This data ften riginates frm a variety f surces, frmats, and types and is generally cnslidated, transfrmed, and laded int ne r mre instances f a database management system usually a ruting database management system (RDBMS) t facilitate a brad range f analytical applicatins. The data warehuse may cnsist f a single large enterprise-wide database, t which users and administratrs cnnect directly, r it may incrprate several smaller systems, called data marts, each f which addresses a specific subject area within the verall warehuse. As a technlgy, data warehusing is the fundatin f the business intelligence capabilities that enable custmer acquisitin, custmer care, and fraud preventin. Overview This tutrial presents the current business landscape and an increasingly ppular business mdel and then explres the fundamental elements f data warehusing frm design methdlgies and implementatin issues t key technlgies. Because the issue f investing immediately in a central, enterprisewide data warehuse r starting small by segmenting data int ne r mre strategic data marts is ften htly debated, the tutrial will als utline the prs and cns f each apprach. Tpics 1. Intrductin 2. Shifting Business Landscape 3. The Evlutin 4. Business Intelligence Capabilities 5. Data Warehusing Fundamentals 6. Methdlgies/Prcesses

2 7. Cnclusin Self-Test Crrect Answers Glssary 1. Intrductin In cmpetitive telecmmunicatins envirnments, custmers chse their service prviders. Tday, this is a reality fr lng-distance, Internet service prvider (ISP), wireless, and sme lcal markets in the United States. Internatinally, cmpetitin exists in cuntries such as the United Kingdm, Australia, and New Zealand. Hwever, deregulatin f the cmmunicatins market is ccurring in the U.S. lcal telephne market, and privatizatin/deregulatin is ccurring acrss Eurpe, Latin America, and Asia. Under cmpetitive cnditins, the custmer becmes the central fcus f the carrier s activities. Custmer requirements nt nly determine service fferings but als shape the netwrk and impact the rganizatinal structure f the carrier t fcus n particular types f custmers. Cmpetitin (and the threat f cmpetitin) has resulted in tw distinct trends in the telecmmunicatins industry: industry cnslidatin In an effrt t cmpete, carriers are lking fr ways t enter new markets and ffer new services. The mst lgical and expedient way t d this is thrugh mergers and acquisitins. cnvergence The integratin f multiple lines f telecmmunicatins services (i.e., vide, vice, data, etc.) has becme an ecnmic reality, and carriers are beginning t ffer multiple services, ften in the frm f packages r bundles f prducts. Telecmmunicatins cmpanies wrldwide are explring business intelligence slutins t achieve cmpetitive advantage. The key slutins fr which telecmmunicatins cmpanies are lking invlve marketing such as custmer retentin, target marketing, and campaign management, custmer-relatinship management, and netwrk business intelligence t streamline netwrk assets. Web PrFrum Tutrials Cpyright The Internatinal Engineering Cnsrtium 2/17

3 2. Shifting Business Landscape The Strategy The business landscape f the telecmmunicatins industry is quickly evlving. The previus mdel, shaped by a handful f cmpetitrs in each cuntry, is being replaced by a mdel shaped by hundreds f cmpetitrs vying fr a glbal presence. Hw d cmpanies survive in this envirnment? There are tw basic strategies that can be pursued. Prduct Strategy Cmpanies can cntinue marketing their prducts t the masses. This marketshare strategy has been very ppular in the telecmmunicatins industry in the past. T cmpete, cmpanies are driven t increase advertising and marketing csts aggressively while discunting their prducts. Unfrtunately, this strategy has driven custmer lyalty t an all-time lw. Fr example, it is nt unusual fr a cnsumer t switch frm Cmpany A t Cmpany B t Cmpany C and back t Cmpany A in the perid f a single year. This may be why cmpanies can reprt a 40 percent discnnect rate ver the perid f a year and still shw an increase in market-share. Clearly, this mdel fr ding business presents significant challenges and ver time threatens t drive prfit margins unacceptably lw. The ultimate evlutin may be similar t what has been seen in the retail industry, where a gd year prduces prfit margins in the tw t three percent range. Custmer Strategy Cmpanies can fcus n tailring prducts t the individual custmer. In this "share f custmer" envirnment, custmers are differentiated in additin t prducts. 1 Crprate resurces are efficiently allcated t custmer care in relatin t the custmer s lifetime value. Thse custmers whse lyalty can be earned and whse lifetime value t the cmpany is high will receive a majrity f the attentin. In cntrast, custmers wh are nt lyal r whse lifetime value is lw will receive a lwer degree f attentin. The result will be an envirnment that ptimizes prfits by nurturing valued custmer relatinships. Essential t this strategy will be the ability t leverage evlving technlgies t accmplish the fllwing: understand the custmers needs and behavirs 1 The One t One Future, Dn Peppers and Martha Rgers, Dubleday Publishing. Web PrFrum Tutrials Cpyright The Internatinal Engineering Cnsrtium 3/17

4 leverage this understanding t identify, develp, and deliver relevant prducts and services Chsing r integrating these strategies and migrating t this new envirnment is ne f the mst prfund decisins facing telecmmunicatins cmpanies acrss the glbe. 3. The Evlutin As we review the evlutin f the telecmmunicatins industry, it is clear that many cmpanies are aggressively mving (r have already mved) frm a business mdel based n a prduct strategy t a business mdel based n a custmer strategy. This envirnment is characterized by custmer relatinships, prduct custmizatin, and prfitability, and is in respnse t pressures transfrming the business landscape thrughut the telecmmunicatins industry. Grwing Cnsumer Demand Custmers (r cnsumers) are expecting cmpanies t understand and respect their needs and desires. In this wrld, the custmer drives the relatinship. It is the rle f the business t hear what the custmer has t say and respnd by delivering relevant prducts and services (what they want) n their terms (hw they want it). Cmpanies can n lnger expect t sell several prducts and services t the masses (mass marketing) but must tailr many prducts and services (i.e., mass custmizatin) t the individual. This is generally referred t as mass custmizatin. Grwing Cmpetitin The ability t refcus a prduct mix in respnse t evlving cmpetitin is a critical success factr fr any business. The key is t be able t anticipate the needs f the marketplace befre ne s cmpetitrs. It is this ability t utpace cmpetitrs that mst cmpanies find difficult r impssible t d, given tday s amalgamatin f technlgies and architectures. Why is this imprtant? Crpratins tday are facing mre and mre deregulatin; mergers and acquisitins are blurring the relatinships t custmers; and glbalizatin f the marketplace and cnsumer is pening up businesses t new avenues fr expansin and, as a result, new cmpetitrs. Therefre, it is mandatry fr a crpratin t restructure itself quickly withut lsing the ability t cmpete. Web PrFrum Tutrials Cpyright The Internatinal Engineering Cnsrtium 4/17

5 Optimizatin The ability t measure and predict return n investment (ROI) is smething that crpratins find difficult t perfrm rapidly. These measurements indicate the health f the crpratin, and the ability t determine them rapidly allws a crpratin t change its directin with minimal lss. Other examples f the need fr ptimizatin include the ability t determine the mst efficient channels fr cntacting custmers, target the apprpriate custmers fr a crpratin s prduct mix, and identify new prduct pprtunities befre the cmpetitin. The Challenge The challenge is that mst infrmatin systems are built arund the prduct strategy business mdel. This has resulted in a variety f prduct-riented systems that effectively run day-t-day peratins. Mving frward, additinal systems are needed t augment capabilities prduced by these business peratin systems with capabilities t deliver best-f-breed business intelligence and business management. These capabilities enable cmpanies t accmplish the fllwing: understand the needs f their business (business intelligence) manage actins based n thse needs (business management) effectively run day-t-day peratins (business peratins) Figure 1 illustrates hw specific capabilities align t each f these areas. Figure 1. Additinal Infrmatin Systems Are Needed t Enable New Business Capabilities Web PrFrum Tutrials Cpyright The Internatinal Engineering Cnsrtium 5/17

6 These capabilities will enable cmpanies t realize the pprtunity f a business landscape characterized by custmer relatinships, custmized prduct delivery, and pprtunity-driven prfit. One f the key enabling technlgies t this evlutin is the data warehuse. Figure 1 illustrates hw the peratinal data stre, as an enabling technlgy, delivers business management capabilities t cmplete the infrmatin slutin. Figure 2 illustrates hw the data warehuse, as an enabling technlgy, delivers business intelligence capabilities t supprt business functins in the telecmmunicatins industry. Figure 2. A Data Warehuse Delivers Business Intelligence Capabilities t Supprt Intelligence Applicatins 4. Business Intelligence Capabilities Strategic Decisin Supprt This is the crnerstne t business intelligence. In this mdel, end users are prvided with intuitive tls t distill infrmatin abut crprate assets and their perfrmance. Crprate assets include custmers, prducts and services, netwrk infrastructure, and emplyees. Typical perfrmance measurements include prfitability, availability, usage, sales, and lifetime value. Cmpanies can nw track key perfrmance measurements, refine custmer segments and scres, and ptimize campaign strategies. Sme f the typical strategic decisin-supprt capabilities in the telecmmunicatins industry include the fllwing: Develp simple reprting capabilities that allw ne t measure and trend key perfrmance metrics; these metrics include the fllwing: Web PrFrum Tutrials Cpyright The Internatinal Engineering Cnsrtium 6/17

7 install and discnnect rates call-center average sales per hur call-center average talk time campaign perfrmance custmer segment lifetime value peak netwrk vlumes uncllected receivables custmer satisfactin Develp cmplex reprting capabilities that allw ne t uncver prblems and discver new pprtunities; typical areas fr analysis include the fllwing: market assessment channel planning cmpetitin assessment strategy and pricing custmer penetratin and prfitability custmer segmentatin prgram definitin recgnitin f patterns relative t custmer behavir and needs Develp statistical mdels that predict custmer needs and behavirs; fr example, ne can build mdels that predict a custmer s likelihd t d the fllwing: buy a new prduct generate high prfitability respnd t cntacts thrugh specific channels (e.g., direct mail, telemarketing, , etc.) nt pay their bill In additin, mdels can be built that predict netwrk grwth and fraud based n traffic patterns in the netwrk. Web PrFrum Tutrials Cpyright The Internatinal Engineering Cnsrtium 7/17

8 Scring and Segmentatin These prvide the mechanisms fr deplying scre and segmentatin rules develped thrugh strategic decisin supprt. Scring prvides prcesses that apply statistical mdels t each custmer (r prspect). A scre frm ne t 100 is then assigned t indicate hw well the custmer fits the mdel. Fr example, suppse that a mdel predicted wh was likely t be a high-usage custmer. This mdel wuld be applied t each custmer and a resulting scre wuld be assigned. A scre f 100 wuld indicate a near-perfect match t the mdel, as ppsed t a scre f ne, which wuld indicate that the custmer did nt fit the mdel at all. Segmentatin prvides a means fr gruping similar custmers. Fr example, ne may segment the custmer base between residential and cmmercial markets. In additin, ne may decide t prvide further granularity by defining segmentatin within these subsegments. Defining custmer segments is the first key step tward defining a custmer management strategy. Campaign Assignment and Management These start where strategic decisin supprt and scring and segmentatin leave ff. Nw that we understand what prducts t deliver, t whm and hw, it is time t set up a campaign t rchestrate the cntact activity. Generally, campaigns cntain six key elements: the list f custmers t be cntacted as part f the campaign the channel t be used in reaching the custmer the prduct, prgram, and service t be ffered the incentive t be used in selling the relatinship relative t ther campaigns the pririty relative t ther campaigns Once the campaign has been defined, it is executed via the cntact management capability. Business intelligence and subsequently the data warehuse plays a key rle in this prcess by prviding capabilities t generate custmer lists (element ne). Elements tw thrugh six are handled as business management activities, as a result f different data, access, and availability requirements. Attempting t perfrm elements tw thrugh six as business intelligence activities (via the data warehuse) is likely t cnstrain ne s infrmatin architecture ver time. Web PrFrum Tutrials Cpyright The Internatinal Engineering Cnsrtium 8/17

9 Putting the Capabilities t Wrk Nw that the tutrial has discussed business intelligence capabilities that will be crucial t surviving in tmrrw s business landscape, it will examine hw these capabilities align t the needs f the business. Custmer retentin will serve as an example. 1. Strategic decisin supprt wuld be used t track key perfrmance metrics relative t custmer install and discnnect activity. This wuld prvide early warning f increasing discnnect activity. 2. If discnnect activity began t grw utside f acceptable limits, strategic decisin supprt wuld be used t analyze why custmers were discnnecting and extraplate impact t prfitability. 3. If the prfitability impacts were nt acceptable, strategic decisin supprt wuld be used t frmulate strategies fr retentin. 4. Once strategies were frmulated, strategic decisin supprt wuld be used t develp predictive mdels that wuld align retentin strategies t the apprpriate custmers. 5. Scring wuld apply these predictive mdels t the entire base f custmers, assigning a scre value between ne and Campaign assignment wuld use these scres and ther relevant data t assign custmer lists t the apprpriate retentin campaigns. 7. Business management wuld initiate these campaigns and manage their executin. 8. As feedback is being returned frm business management, input wuld be used by strategic decisin supprt t refine retentin strategies. As may be deduced, a number f capabilities are needed t supprt a single business need (e.g., retentin), and these capabilities are integrated thrugh the business prcess. What may nt be quite as evident is that these capabilities can be reused t supprt ther business needs, such as custmer care r fraud. Capabilities are essential t prviding telecmmunicatins cmpanies with the ability t respnd t the changing needs f their custmers and the marketplace quickly and cst effectively. The data warehuse is a fundamental enabler t delivering business intelligence capabilities. Web PrFrum Tutrials Cpyright The Internatinal Engineering Cnsrtium 9/17

10 5. Data Warehusing Fundamentals One f the technlgy areas that will be critical t supprting the business mdel discussed in Tpic 4 is data warehusing. Data may be viewed and analyzed frm the warehuse in a number f ways. One very cmmn retrieval paradigm invlves the use f an n-line analytical prcessing (OLAP) engine. OLAP is the term mst ften used t describe the brad range f analysis fr which the data warehuse is used. It is different frm n-line transactin prcessing (OLTP) in a variety f ways, but the key difference is that OLAP fcuses n prcessing queries t the database, whereas OLTP systems prcess transactins in which infrmatin is entered int the database. OLTP applicatins are rarely, if ever, built n data warehuses. OLAP prducts can ffer a range f advanced analysis capabilities, such as Tp N, data pivting fr multidimensinal analysis, statistical functinality, the ability t drill frm high-level data t successive levels f detail fr iterative analysis, etc. OLAP engines can be leveraged t deliver high perfrmance and extended analytics fr ad hc query and reprting tls, as well as fr specialized packaged applicatins r custm executive infrmatin systems (EIS). Data warehuses can als be used t supprt sphisticated peratinal analysis functins such as custmer scring systems (described earlier) and fraud detectin applicatins. All f these facilities may be used in cncert with a separate prcess knwn as data mining. Data mining is the practice f plling data fr interesting elements r anmalies withut actually having t pse specific questins r queries. Data mining is very bradly defined in the industry, and cnsequently, data mining sftware can vary greatly in terms f functinality and scpe. It usually references sampled data and is ften custmized t an individual rganizatin s needs. Figure 3. Simplified Data Warehuse Architecture Web PrFrum Tutrials Cpyright The Internatinal Engineering Cnsrtium 10/17

11 6. Methdlgies and Prcesses Architecture: Enterprise versus Data Marts As previusly mentined, the chice between a single enterprise-wide warehuse architecture and an architecture cnsisting f many smaller data marts is a pint f sme cntentin. The difference is nt necessarily ne f cst, althugh a wellplanned enterprise slutin may require a larger investment initially t accunt fr the mre extensive data analysis and mdeling up frnt, as well as t avid cntinual hardware upgrades as the warehuse grws. A data mart slutin is typically mre mdular, thus exhibiting a mre linear cst curve. Over the lng term, hwever, csts tend t even ut, and bth systems can allw fr expansin and grwth. The chice f architecture usually depends mre heavily n rganizatinal factrs. Depending n the situatin, strng arguments can be made fr bth sides. Prs and Cns Decisin-supprt systems existed lng befre the term data warehuse was cined, ften starting in the marketing, sales, and finance departments, with each grup pulling the data they required dwn frm the legacy systems int small databases n their lcal servers. These slutins were ften implemented independently and withut assistance frm infrmatin services (IS). Cnsequently, results tended t be incnsistent, particularly as cmpared t results btained by sister rganizatins with similar but separate decisinsupprt systems f their wn. Thus, early wisdm in the data warehusing market favred a single, central enterprise system that wuld serve all rganizatins needs. Althugh sme cmprmises might be necessary in serving the needs f the many, this type f slutin wuld at least ensure cnsistency f data and scpe acrss rganizatins. In additin, the infrmatin was typically mre reliable, as the prcess f acquiring and cleansing data was mre deliberate and mre carefully managed. Hwever, the enterprise data warehuse apprach has a drawback. It takes time, and it takes crdinatin between many grups acrss ptentially many different rganizatins within a cmpany. Sme pineers f this apprach ran int lng delays in their warehuse implementatins as well as crdinatin stumbling blcks that either terminated r significantly set back their deplyments. In particular, cmpanies that are very cmpartmentalized in their structure d nt lend themselves easily t an enterprise-wide slutin. In these cases, integrated data marts can ffer greater custmizatin, less cntentin fr system resurces, and greater independence fr participating rganizatins. It is imprtant, hwever, fr the reasns stated earlier, that particular care be taken in planning Web PrFrum Tutrials Cpyright The Internatinal Engineering Cnsrtium 11/17

12 the data acquisitin, transfrmatin, and cnslidatin stages fr a data mart. Centralized cnslidatin can ften help minimize data incnsistency between data marts. A sund deplyment strategy is t harness the benefits f bth appraches by engaging a few key end-user grups as well as the IS rganizatin and designing a skeletn architecture fr the brad enterprise data warehuse. Hwever, rather than begin by trying t lad, cleanse, deply, and manage all f the enterprise data, select ne r tw key applicatins t fcus n first, define the data marts required t supprt them, and start with a manageable prject. Then, as new applicatins are brught n-line, merge them int the enterprise warehuse architecture and implement them ne at a time. This strategy is smetimes referred t as the architected data mart apprach. Critical Success Factrs Knw Yur Gals Perhaps the mst imprtant success factr in the data warehuse is t keep ne s lng-term gals in mind. It is easy t let a warehuse prject explde beynd the initial bundaries set fr it. One user grup finds ut that it has been excluded frm initial rllut and demands t be included. The initial target user grup decides it wuld be even better if it culd d churn management and pricing analysis during phase ne, rather than wait until phase tw t bring the pricing analysis system n-line. The IS directr decides it makes sense t define, build, and lad the entire warehuse infrastructure befre trying t deliver data t end users. Clearly, it is almst impssible t eliminate scpe creep cmpletely. Valid issues arise and must be dealt with. Hwever, it is generally a bad sign when bjectives change significantly after final sign-ff r, wrse, after implementatin has begun. Whenever pssible, initial gals t which all are agreed shuld be maintained. Exceptins will always ccur, but it is an imprtant rule f thumb. Perfrmance The data warehuse will fail if users d nt get answers back in a timely fashin. This is why parallel database technlgy, advanced indexing, data mdeling, aggregatin, and sampling are s critical t the success f the data warehuse prject. This is an area in which investment makes sense nt nly in technlgy, but in experts wh knw hw t maximize the utility f the technlgy. Web PrFrum Tutrials Cpyright The Internatinal Engineering Cnsrtium 12/17

13 Data Vlumes: Lads and Management One f the characteristic challenges f a telecmmunicatins business is managing enrmus vlumes f data. Nt nly d telecm systems generate huge numbers f detailed call recrds (and thers), but the recrds themselves are very large, and it is nt always pssible t knw what, if anything, can be filtered ut f the warehuse. Cmplicating matters is that cmmn telecm applicatins, like fraud detectin, require real-time data feeds fr ptimal effectiveness. As a result, telecm data warehuses must be able t handle tremendus insertin rates, as well as manage databases that will likely grw int the multiterabyte range. Building a system with technlgy that will nt scale effectively as data vlumes grw can be ne f the mst frustrating (and expensive) experiences a cmpany can endure. Relatinship between IS and Users In any data warehuse implementatin, it is critical that IS and the user cmmunity be in agreement abut the bjectives f the warehuse and be in cnstant cmmunicatin thrughut the prject. A prject driven slely by IS runs a significant risk f slw adptin, lw return n ROI, r even utright failure. A prject slely driven by the user cmmunity risks a duplicatin f effrt, a lack f integratin with ther critical systems acrss the rganizatin, and an unmanageable enterprise envirnment. Executive Spnsrship Data warehuses are very large, expensive, cmplex systems. All warehuse prjects will encunter tumultuus perids, whether due t technlgical setbacks, staffing issues, insufficient infrastructure, r pr cmmunicatin. And this is nt an exhaustive list. All prblems are reslvable, but if there are n spnsrs at the senir management level, these prblems can quickly take n mnumental prprtins. By spnsrs, we mean peple wh understand the business pain being addressed by the prject and believe whleheartedly that building the data warehuse is the best rute t reslving that pain. A pwerful executive spnsr can help knife thrugh challenges befre they impact prject mmentum. A lack f executive supprt raises cnstant dubts abut a prject and can lead t multiple starts and stps in the implementatin, badly demralizing the prject team and the business cmmunity. 7. Cnclusin As telecmmunicatins markets becme increasingly cmpetitive, the ability t react quickly and decisively t market trends and t tailr prducts and services t individual custmers is mre critical than ever. Althugh data vlumes Web PrFrum Tutrials Cpyright The Internatinal Engineering Cnsrtium 13/17

14 cntinue t increase at an astunding rate, the prblem is n lnger simply ne f quantity; at the heart f the issue is hw cmpanies are using their infrmatin. Increasingly, particularly in the telecmmunicatins industry, it is imprtant t understand custmer preferences and behavirs. A data warehuse can be a very effective means f rganizing and analyzing the cmplex barrage f infrmatin generated in ne s business and helping t generate a mre effective business mdel fr keeping ne s custmer base happy and prfitable. Self-Test 1. Data warehusing is the prcess f integrating enterprise-wide crprate data int multiple repsitries. 2. The prduct strategy business mdel has increased custmer lyalty. 3. A scre f indicates a near-perfect match between the custmer and the mdel. a. ne b A scre f indicates that the custmer des nt fit the mdel at all. a. ne b Capabilities that supprt a single business need may be reused t supprt ther needs. 6. The fcuses n prcessing queries t the database. a. OLAP Web PrFrum Tutrials Cpyright The Internatinal Engineering Cnsrtium 14/17

15 b. OLTP 7. The prcesses transactins in which infrmatin is entered int the database. a. OLAP b. OLTP 8. Data mining invlves psing specific queries r questins t the database. 9. Csts are abut even fr enterprise versus data marts. 10. In any data warehuse implementatin, IS and the user cmmunity must be in cnstant cmmunicatin. Crrect Answers 1. Data warehusing is the prcess f integrating enterprise-wide crprate data int multiple repsitries. See Definitin. 2. The prduct strategy business mdel has increased custmer lyalty. See Tpic 2. Web PrFrum Tutrials Cpyright The Internatinal Engineering Cnsrtium 15/17

16 3. A scre f indicates a near-perfect match between the custmer and the mdel. a. ne b. 100 See Tpic A scre f indicates that the custmer des nt fit the mdel at all. a. ne b. 100 See Tpic Capabilities that supprt a single business need may be reused t supprt ther needs. See Tpic The fcuses n prcessing queries t the database. a. OLAP b. OLTP See Tpic The prcesses transactins in which infrmatin is entered int the database. a. OLAP b. OLTP See Tpic Data mining invlves psing specific queries r questins t the database. Web PrFrum Tutrials Cpyright The Internatinal Engineering Cnsrtium 16/17

17 See Tpic Csts are abut even fr enterprise versus data marts. See Tpic In any data warehuse implementatin, IS and the user cmmunity must be in cnstant cmmunicatin. See Tpic 6. Glssary EIS executive infrmatin system IS infrmatin services ISP Internet service prvider OLAP n-line analytical prcessing OLTP n-line transactin prcessing RDBMS ruting database management system ROI return n investment Web PrFrum Tutrials Cpyright The Internatinal Engineering Cnsrtium 17/17