Shining the Light on Dark Data Through Data Preparation and Cognitive Analytics 10.24.16
Welcome Dan Potter CMO, Datawatch dan_potter@datawatch.com Jon Pilkington CPO, Datawatch Jon_pilkington@datawatch.com 2
Dark Data The Other 88% 12% Relational, Excel, Hadoop, Salesforce, etc. 88% Reports, Web Pages, JSON, Log Files, etc. Make Better Decisions 3
What is Data Preparation? XLS Access Manipulate Enrich Makes all your data analytics-ready Combine Load Data Warehouse Analytics Operational Processes 4
Value of Self-Service Data Preparation SPEND LESS TIME PREPARING DATA Analysts waste up to 80% of their time preparing data versus analyzing $22,000 per year, per analyst wasted 5
Datawatch and IBM IBM resells Datawatch for data preparation with: IBM Cognos Analytics IBM Watson Analytics IBM Content Manager on Demand 6 6 World of Watson 10/25/2016 2016
40,000 Organizations Use Datawatch 7 19
Analysts Need to Evolve Salesforce Data Current Sales Pipeline Reports Need time to retype the data I have, as it s only in monthly PDF reports 8
Analysts Need to Evolve 9
Data is Still Scattered Across the Enterprise! Other BI Apps Office 1000x of objects Since there is no central place to go and search, it is: BI platforms 10x-100x of reports EXPENSIVE 40%-60% of a knowledge workers` time is spent searching for and trying to understand information Other reporting tools 10x-100x of reports/ spreadsheets, etc. UNGOVERNED it is an unpredictable and undocumented process and typically not a repeatable task UNSECURED there is either no or little security control FRUSTRATING it`s complex, frustrating and an unsupported process TIME CONSUMING people are overloaded by mail, phone calls, meetings Data Warehouse & Case tools ETLs and DQ Tools Other data sources 10
The Evolution of the Data Analyst: What would a Data Utopia look like for a Business Analyst? As a Business Analyst, I need to acquire and prepare data from: any source any place any time any device any size When I need data, I can easily find and use ANY data that has been made accessible to me in my ecosystem User Generated Semantic Layer (learned from others) is available to me to obfuscate the complexity of raw data. Connectors are a thing of the past. I can just connect to each system without additional setup. I have a Social Network of Raw Data (RD) and Curated Data Sets (CDS) at my Fingertips A Social Data Network allows me to Subscribe and Follow other Analysts that I find Interesting Powerful search allows me to find data by user, by type, by application, by unique value Combination of machine learning and powerful search helps me understand how all datasets are related to one another Redundancy of effort is a thing of the past, CDS are sharable and reusable and saves me time CDS can be Enterprise Approved / Stamped A Social Data Network helps me contribute to the greater good of the Enterprise by sharing my CDS Data Lineage gives me confidence in the data I am working with My data (raw and CDS) understands how it is related to other data (by use case/ by persona) Curated data that is not for my eyes is unavailable or redacted for my persona 11
Analyst Utopia Curated Data Sets All Data Sources CLOUD STORAGE Who RESEARCH DATA What CLOUD APPS Where CLEANSING DATA When Data Type ON PREMISE APPS DESKTOP DATA Role Created Problem Solved SHARED DRIVE DOCUMENTS STREAMING DATA ADVANCED SEARCH Data Sources / Tractability Indexed Unique Values Who else can see (by role) 12
Socialization of Data Social media and mobile applications have dramatically increased end user expectations on the availability and timeliness of enterprise data. Platform Key Concepts Adopted in Datawatch Follow Follow relevant users and sources Relevant content Intelligently serve data and notifications Network of influencers Understand the utilization of data in the context of user roles Marketplace Marketplace of enterprise and public datasets Search Intuitive search of metadata and data Crowdsourced reviews User ratings on quality and relevancy Know what to avoid Machine learning Understand patterns of use and success Automatically recommend and apply likely actions 13
Socialization of Data Oracle SAP SFDC Eloqua Marketo Great Plains Net Suite Zendesk Operational Data Sources Monarch Artifacts Promote Marketing Analyst Workspaces Marketing Team Files CDS Connectors Sales Analyst XLS CSV MOB Exports TDE QUX ParBI Cognos Watson Other CDS Sales Team Finance Analyst Futures All Data Finance Team Finance Team IT Team 14
Value Throughout the Organization IT and BI Teams Analysts Information Workers Stronger governance Higher productivity Better support the business Better decisions using cognitive analytics Improved customer understanding Leverage existing investments Greater quality and trust Optimize business processes New business model & revenue opportunity 15
Demonstration 16
Power of Cognitive Analytics 17
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Thank You! Dan Potter CMO, Datawatch dan_potter@datawatch.com Jon Pilkington CPO, Datawatch Jon_pilkington@datawatch.com 19