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Business Intelligence 9 11 08 Cio Breakfast 1


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Business Intelligence 9 11 08 Cio Breakfast 1

  1. 1. BUSINESS and COMPETITIVE INTELLIGENCE Paul Gray CIO Breakfast Round Table 9-11-08
  2. 2. Aims Of This Talk <ul><li>To tell you what the shouting is about </li></ul><ul><li>Help you decide whether business intelligence is: </li></ul><ul><ul><li>Simply a new name </li></ul></ul><ul><ul><li>A repackaging of DSS in a more appealing wrapper </li></ul></ul><ul><ul><li>The true future of decision support </li></ul></ul><ul><li>Examine the ROI from BI and CI </li></ul><ul><li>Examine the impact of Web 2.0 </li></ul><ul><li>Examine business considerations </li></ul>
  3. 3. Definition Of BI Systems <ul><li>Business intelligence systems combine: </li></ul><ul><ul><li>Data gathering </li></ul></ul><ul><ul><li>Data storage and </li></ul></ul><ul><ul><li>Knowledge management with </li></ul></ul><ul><ul><li>Analysis </li></ul></ul><ul><li>to evaluate complex corporate and competitive information and present the results to planners and decision makers. </li></ul><ul><li>Objective: Improve timeliness and quality of the input to the decision process </li></ul>
  4. 4. Implicit In Definition <ul><li>Business intelligence systems provide: </li></ul><ul><ul><li>actionable information and knowledge </li></ul></ul><ul><ul><li>at the right time </li></ul></ul><ul><ul><li>in the right location </li></ul></ul><ul><ul><li>in the right form </li></ul></ul>
  5. 5. BI Relation to Other Software Data Mining DSS/ EIS Business Intelligence Data Warehouse Knowledge Management CRM/dB Marketing Web 2.0 GIS OLAP
  6. 6. What BI does <ul><li>Strategic use </li></ul><ul><ul><li>Corporate performance management </li></ul></ul><ul><ul><li>Optimizing customer relations </li></ul></ul><ul><ul><li>Packaged standalone BI applications </li></ul></ul><ul><ul><li>Management reporting of BI/CI data </li></ul></ul><ul><li>Tasks </li></ul><ul><ul><li>Creating forecasts and estimates of future direction </li></ul></ul><ul><ul><li>“ What if” analysis of alternative scenarios. </li></ul></ul><ul><ul><li>Ad hoc access to answer non-routine questions. </li></ul></ul><ul><ul><li>Strategic insight </li></ul></ul>
  7. 7. Implications <ul><li>Ordinary reports of a firm’s performance and competitor performance (what BI software gives) is not enough. Need analysis to put it in context </li></ul><ul><li>For too many firms, BI (like DSS and EIS before them) is still inward looking and is used only by a small subset of people </li></ul>
  8. 8. BI Application <ul><li>Company that sells natural gas to homes </li></ul><ul><ul><li>builds dashboard to support </li></ul></ul><ul><ul><ul><li>Operational performance metric measurement </li></ul></ul></ul><ul><ul><ul><li>Real time decision making </li></ul></ul></ul><ul><ul><li>Result: No. of repeat repair calls reduced saving $1.3 million </li></ul></ul>
  9. 9. Return on Investment <ul><li>Costs </li></ul><ul><ul><li>Upfront cost and upkeep are high ($200/seat for Cognos) </li></ul></ul><ul><ul><li>Cost efficiencies in IT can be forecast but not in BI or CI </li></ul></ul><ul><li>Benefits </li></ul><ul><ul><li>Cost reductions don’t pay cost </li></ul></ul><ul><ul><li>Get new opportunities, discover problems, and avoid difficulties </li></ul></ul>
  10. 10. Return on Investment <ul><li>Costs include : </li></ul><ul><li>Additional hardware </li></ul><ul><li>Large amt. of software </li></ul><ul><li>Purchased external data </li></ul><ul><li>A dependent data mart for BI </li></ul><ul><li>Analysts and support staff </li></ul><ul><li>Hardware, software update </li></ul><ul><li>and maintenance </li></ul><ul><li>User time thinking about BI outputs </li></ul><ul><li>Benefits mostly soft; </li></ul><ul><li>include: </li></ul><ul><li>Hope for ‘big bang’ returns in the future (but can’t forecast them or their timing) </li></ul><ul><li>Better understanding of the business and the competitor’s business </li></ul>
  11. 11. Change: BI for the Masses <ul><li>BI tools are moving to the whole mass of knowledge workers, not just few specialists </li></ul><ul><li>A way of closing gap between analysis and operations, moving to multiple levels in the organization </li></ul><ul><li>Previously, typical analyst use is ‘one-off’ study </li></ul><ul><li>Large deployments of BI include 70,000 at French Telecom, 50,000 at US Military health systems. Other examples at 20,000 users </li></ul>
  12. 12. Web 2.0 Example:5 Bashups
  13. 13. Web 2.0 Example: GIS,BI
  14. 14. WEB 2.0 IMPLICATIONS <ul><li>Does not require much end-user skills </li></ul><ul><li>(<20% of users in most orgs. Use reporting, ad hoc query, and online analytic processing) </li></ul><ul><li>Web 2.0 results in intuitive interface, better data mgmt. and access </li></ul><ul><li>Get “bashups” in std. format from variety of sources. </li></ul><ul><li>Brings together, e.g., GIS, on-demand BI, external sources (e.g., Web) </li></ul><ul><li>Lets businesses see value in analytics </li></ul>
  15. 15. WEB 2.0 EXAMPLE <ul><li>Mass. Housing Finance Agency </li></ul><ul><ul><li>Mapping capabilities </li></ul></ul><ul><ul><li>External MapInfo Software </li></ul></ul><ul><ul><li>Cognos Dashboard </li></ul></ul><ul><ul><li>Now used by 300 , not just 12 analysts </li></ul></ul><ul><ul><li>Future: show housing units, loan data, public transportation visually on one screen </li></ul></ul><ul><li>In insurance: link claims and wellness data to allow employers to evaluate ROI of different programs and benefits </li></ul>
  16. 16. CHALLENGES <ul><li>Data quality (governance, master data management, performance indicators) </li></ul><ul><li>Agreeing on common data definitions, meaning of performance indicators </li></ul><ul><li>Having right infrastructure in placed </li></ul><ul><li>In some firms, IT’s relies on ‘name’ vendors (SAP,, not small startups in BI space </li></ul>
  17. 17. WEB 2.0 BI AND THE CIO <ul><li>Put more capabilities in user hands. Make IT enabling infrastructure </li></ul><ul><li>IT should allow users to get data without programming </li></ul><ul><li>“ IT should get out of developing interfaces and become involved in data quality and data integration </li></ul><ul><li>IT should not fight emerging BI technologies to enforce standards –Schlegel, Gartner group. Should put them into the BI architecture to avoid rogue BI capabilities </li></ul><ul><li>Use a self-service BI strategy to reduce cost, speed delivery </li></ul>
  18. 18. Competitive Intelligence <ul><li>“ No more sinister than keeping your eye on the other guy, albeit secretly” –Claudia Imhoff </li></ul><ul><li>More formal definition by Society of Competitive Intelligence Professionals (SCIP) </li></ul><ul><li>“ CI is a systematic and ethical program for gathering, analyzing, and managing external information that can affect your company’s plans, decision and operations” </li></ul>
  19. 19. Definition Of CI In Practice <ul><li>CI ensuring marketplace competitiveness </li></ul><ul><li>Through understanding: </li></ul><ul><li>-competitors </li></ul><ul><li>- over-all competitive environment </li></ul><ul><li>-------- </li></ul><ul><li>Can use whatever you find in the public domain to make sure you’re not surprised by your competitors. </li></ul>
  20. 20. Examples Of CI <ul><li>Comshare bought a competitor after monitoring the competitor’s hometown newspaper </li></ul><ul><li>Texas Instrument made $100m acquisition by figuring out competition’s potential bids </li></ul><ul><li>Merck developed counter-strategy about competitor’s upcoming product, saving $200M </li></ul>
  21. 21. Sources of CI <ul><li>Government information </li></ul><ul><li>Online databases </li></ul><ul><li>Interviews and surveys </li></ul><ul><li>Special interest meetings such as SIM </li></ul><ul><li>Competitors, suppliers, distributors, customers </li></ul><ul><li>Media (newspapers, journals, wire services, financial reports, speeches by executives bragging about their firm) </li></ul>
  22. 22. Competitive Intelligence Tools <ul><li>Simulations to test ‘what if’ conditions </li></ul><ul><li>Data mining about competitor & firm </li></ul><ul><li>Track patents to assess competitor technologies </li></ul><ul><li>Scan public record, Internet, press release, mass media </li></ul><ul><li>Talk with customers, suppliers, partners, industry experts, sales people </li></ul>
  23. 23. Notes on CI <ul><li>Problem is not lack of information but too much information </li></ul><ul><li>Once you start CI, you try to find ways to make task of finding out about you more difficult. </li></ul><ul><li>Get CI, CCI, CCCI, … C n I </li></ul><ul><li>Same game is played in politics, int’l competition </li></ul>
  24. 24. BI Market <ul><li>Market size (IDC) </li></ul><ul><ul><li>$6.3 billion (2006) </li></ul></ul><ul><li>Trend in pre-built analytic applications because home-built systems take too long (>6 mos.) and cost too much ($2-3 million) </li></ul>
  25. 25. VENDORS-Gartner 2008
  26. 26. Managerial Issues <ul><li>Is BI an oxymoron? </li></ul><ul><ul><li>BI is really about understanding your own position, your customer, your competitor </li></ul></ul><ul><ul><li>An important part of planning and operations </li></ul></ul><ul><li>CI is a way of finding out about your market position </li></ul>
  27. 27. Managerial Issues <ul><li>What do I know once I deploy BI? </li></ul><ul><ul><li>Capabilities available in firm </li></ul></ul><ul><ul><li>State of the art, trends and directions in the markets </li></ul></ul><ul><ul><li>The technologies and regulatory environment </li></ul></ul><ul><ul><li>Competitor actions and their implications </li></ul></ul>
  28. 28. Managerial Issues <ul><li>What capabilities do investments in BI create? </li></ul><ul><ul><li>Complex corporate and competitive information for planners and decision makers </li></ul></ul><ul><ul><li>Improved timeliness and quality of input to the decision process </li></ul></ul><ul><ul><li>(Occasionally) major breakthrough </li></ul></ul>
  29. 29. Managerial Issues <ul><li>How do you gather, transfer BI? </li></ul><ul><ul><li>BI a form of knowledge; includes both explicit and tacit knowledge </li></ul></ul><ul><ul><li>Some knowledge bought (scanner data), other created internally from analysis of public and private data </li></ul></ul><ul><ul><li>Must disseminate to many people in firm; customize by individual, group </li></ul></ul>
  30. 30. Managerial Issues <ul><li>Organization for BI? </li></ul><ul><ul><li>Not necessarily; both centralized and decentralized org. work </li></ul></ul><ul><li>What technologies are available? </li></ul><ul><ul><li>Specialized software packages, many still quite crude </li></ul></ul>
  31. 31. CONCLUSIONS <ul><li>Business Intelligence is a part of DSS but certainly not all of it. </li></ul><ul><li>BI name gives DSS a new skin. Semantics matter </li></ul><ul><li>The technology for BI and CI is getting better, broader, and more universally available. Web 2.0 is coming </li></ul><ul><li>The capabilities of the DSS analyst in business improve as both structured and unstructured data grows </li></ul>
  32. 32. CONCLUSIONS <ul><li>BI and CI are steps along the way. They are the short-term future of DSS in the commercial world. </li></ul><ul><li>In the long term, we will inevitably find new ways of thinking about and solving decision problems. </li></ul>