ANALYTICS COMPETENCIES - MANAGING BIG DATA & BI

Transcription

1 LUNCHTALK Series ANALYTICS COMPETENCIES - MANAGING BIG DATA & BI Presented by Michael Hartung

2 Agenda FREE WEBINAR Trends in Analytics and Business Intelligence Analytics Structure and Definition Big Data Data Structures Business Intelligence Excel - Analytics

3 Analytics Trends (Retail Study) Analytics applicable for all Businesses Mobile and timely Analytics Utilize Analytics in all Function Utilize internal and external data availbale Omni Data Channel Integration Invent new Standards and Tools based on Analytics

4 Trends for Analytics Advanced Analytics not only for MNC or Specialists. Self Service Analytic Tools and BI systems able support all kind of Businesses and Industries Mobile and timely Analytics is fully realized, Even checking BI on Mobile Phone o be always up to date There plenty of data available, make use of them. Identify relevsnt data requirements and sources. Ulilize internal and external data sources. Omni Channel Data integration is key. Integrate data from different internal sources to see the big picture and what is really going on to identify drivers for decision making. Utilize Analytics in the whole organization. Not only Sales and marketing benefit from Analytics, in other functions (e.g.infrastructure) often the benefit is much higher Update company Standards. Invent new procesdures based on data insights (marketing Mix, Real Time Inventory )

5 Manage your Development Analytics Buiness Intelligence Data Mining Online Analytical Processing (OLAP) includes software enabling multi dimensional views of enterprise information which is consolidated and processed from raw data with a possibility of current and historical analysis. Analytics helps make predictions and forecasting of trends and relies heavily on statistical and quantitative analysis to enable decision making concerned with future predictions of business performance. Business Performance Management tools concerned with setting appropriate metrics and monitoring organisational performance against these identifiers. Benchmarking tools provide organisational and performance metrics which help compare enterprise performance with benchmark data, to industry average, for example. Predictive Analysis deals with data analysis, turn it into actionable insights and help anticipate business change with effective forecasting. Text Mining software helps analyse non structured data, such as written material in natural language, in order to draw conclusions for decision making. Data Mining involves large scale data analysis based such techniques as cluster analysis, anomaly and dependency discovery, in order to establish previously unknown patterns in business performance or making predictions of future trends.

6 Analytics Steps Wisdom Application/Transformation of Expertise Expertise Understanding Context / Prescriptive Strategizing Forecasting / Predictive Knowledge Diagnostic /Patterns / Trends Analytics: The discovery, interpretation, and communication of meaningful patterns in data. Especially valuable in areas rich with recorded information, analytics relies on the simultaneous application of statistics, computer programming and operations research to quantify performance. Analytics often favors data visualization to communicate insight. Information Descriptive /Patterns / BI Data Structures / Qualities Business Intelligence: The tools and processes needed to transform data into information and information into drivers for actionable Business Decision

7 Analytics Structure

8 Analytics Areas Integration Project Stakeholders Profitability Analytics Areas Operations Market Marketing & Sales Procurement Analytics most beneficial if implemented in the whole organizations, not only Sales & Marketing Profitability Discount, Margin, Promotions, Incentives, Revenue analysis Market analytics Competition, Pricing strategy, Demand optimization Marketing & Sales analytics Procurement analytics Operations analytics Inventory, admin, infrastructure, finance, Departments, LOB Stakeholder analytics Customer, Vendor, Supply chain, Employees, Contractors, Agents Project analytics Time, Efforts, Risk, Quality, Health Integration analytics Sum of all parts. Better be integrated and not in Silos

9 Samples of Analytic Areas Customer Analytics, which aims to maximise the value of each customer and enhance customer s experience; Human Capital Productivity Analytics, provides insight into how to streamline and optimise human resources within the organisation; Business Productivity Analytics, refers to the process of differentiating between forecasted and actual figures for inputs/outputs conversion ratio of the enterprise; Sales Channel Analytics, aims to optimise effectiveness of various sales channels, provides valuable insight into the metrics of sales and conversion rates; Supply Chain Analytics offers the ability to sense and respond to business changes in order to optimise an organisation s supply chain planning and execution capabilities, alleviating the limitations of the historical supply chain models and algorithms. Behaviour Analytics helps predict trends and identify patterns in specific kinds of behaviours.

10 Big Data

11 Optimize your Training Experience

12 Big Data & Data Mining Paramenter on Data Data Availabilist Source Connectivity Data Quality Timeliness Relevance

13 BI Competencies BI Center of Competence For Effective BI and Analytics different competency areas to be combines

14 BI Architecture ETL Layer (Extract Transform Load)

15 Business Intelligence

16 Failures in BI Implementation An increasing number of organisations are making BI and analytics functionality more broadly available to all decision makers inside and outside the organisation Gartner s research from 2012 on BI says 70% to 80% of corporate business intelligence projects fail due to a poor communication between IT and business, the failure to ask right questions or think about the real needs of the business (Goodwin, 2010). The success of BI implementation is questionable; about 60 to 70% of BI applications fail due to the technology, organisation culture and infrastructure issues (Lupu et al., 2007). Given the failure rate of the BI projects, it is important to understand the critical success factors for the Business Intelligence initiatives in the industry. Evolution in Business Intelligence (Complexity) Software Application & Tools Business & Management Perspectives

17 Success Factors for Analytical Implementation Analytical capabilities business users have the tools and knowledge to leverage the data into information Data Integration integration of data from heterogeneous systems Information content quality consistency and accuracy of information available in the BI system Information access quality the provision of information corresponds to business user needs Use of information in business processes exposes the problematic aspects of current business processes and makes stakeholders aware of them Analytical decision making culture the decision making process is well established and known to its stakeholders Organizati on IT Business Managem ent 1. Alignment between Business & strategy 2. Top Management Support 3. Effective Information? Data? BI Governance 4. Flexible technical Framework 5. Change management 6. Organisation s Analytical Culture 7. Balanced Team Composition 8. Adequate Resources & Funding on efforts 9. Business Driven & iterative development approach 10. Availability of necessary Tools, training & support

18 Implementation Cyle of Analytics & BI Improvement Analysis Utilization Design Development Implementation of Analytical Structures based on CRISP Data Mining

19 Analytics via Excel Key Steps Download relevant Data into Excel Structure and Format Data to identify Patterns Create Dashboards and make Data Visual Include Simple Models via mathematical formulas in Excel

20 Data Analytics in Excel Data Mining and preparation Business intelligence and Dashboards Analytical Tools Filter & Sorting Graphs Formulas Pivot Tables Summary Tables Data Analytics

21 Sample Dashboards in Excel

22 Analytics Support via Excel v

23 Q & A Thank You For further questions, please contact: Michael Hartung mfh1online@gmail.com