Eliminate Dark and Dirty Data

Transcription

1 Eliminate Dark and Dirty Data

2 ECM is dead Long Live ECM

3

4 Why didn t they come? Deep, un navigable folder structures Application of Metadata and Classifications Find-ability Scope not well defined All Resulting in Low User Adoption

5 What is Dark Data? The information assets that organizations collect, process and store during regular business activities, but generally fail to use for other purposes. Up to 90% of Big Data is Dark Data.

6 Dark Data

7 Defining Data Structured Data CRM, ERP Semi Structured Data Invoices, PO s Unstructured Data Everything else

8 Dark Data

9 Types of Content

10 The Dark Challenge Data of By 2015 there will be over 4.1 Billion accounts. By 2015 users will be sending 190 Trillion per year.

11 Challenges with Content Average of 40-60% YoY data growth Cost to store 1TB for 1 year starts at $3,212 Cost to review 1GB = $3,000 -$18,000 # of Companies that will store more than 1PB by 2020 = 100,000 1PB = 1 Million GB Cost to review 1PB = $3 Billion to $18 Billion 80% of IT infrastructure costs are used to support redundant, obsolete data.

12 ediscovery

13 But

14 Adlib and Content We have about 20GB within SharePoint We have 750GB in FileShares 730GB is unstructured content We have 150 employees with about 50GB of unstructured data each WE HAVE OVER 8TB of content sitting around.

15 Enter Information Governance

16 What is Information Governance?

17 It s what data you have and where you have it

18 Information Governance Strategy

19 The Evolution of ECM begins Tag Content Workflow Analytics

20 Unstructured Content focus areas Knowing what you have Assigning appropriate Access Personally Identifiable Information

21 Unstructured Content: Where do you start? Largest pockets: File Shares ECM

22 Unstructured Content, What are you doing today? Currently 300 people 5-10 M Artifacts/month Data doubling every 24 months

23 Unstructured Content: What are people doing? Manual processes with no end in sight File Analysis it s not good enough Big Data Analytics...boiling the ocean File Analysis Big Data Analytics

24 File Analysis Associates basic metadata Enables you to find exact matches based on Filename Filesize etc

25 Big Data Subjective content only doesn t solve it on its own Use cases centered on connecting structured analytics with unstructured and undisciplined information e.g. social networking It s a process, it s a process, it s a process

26 Unstructured Content: How to get quick wins? File Intelligent Big Data Statistics Content Analytics Analysis

27 Intelligent Content Analysis Enhancing it with a human approach independent of content type the flow Leverages File Analysis & Big Data Analytics Delivers an objective approach

28 Content Analysis: Flow

29 Content Analysis: Imagery

30 Content Analysis: Text

31 Content Analysis: The Result Fingerprint AYT76A033DE

32 Extending Beyond Content Analysis: Why? Delivers: An objective model of your Unstructured Content. An on-going governance of your Content

33 Unstructured Content: Enemy or an Ally? You can t protect what you don t know you have

34 Intelligent Content Processing TAG IT Grouping of Content De-dupe CAPTURE IT Get Attributes PROCESS IT Compress Redact Convert Preserve

35 The Evolution of ECM begins Content Intelligent Analysis Workflow Tag Content Analytics

36 Content Problems? Get Content Coaching! Content Coaching is ideal if your organization: Deals with large volumes of documents Requires high-fidelity, compliant document conversion, & archiving Needs to improve the management of unstructured content Is challenged by industry/regulatory demands Discovery Discussion Delivery Book your Session Today. Let the Content Experts Come to You To book your Content Coaching session : Call: ContentCoaching@adlibsoftware.com

37 Darkness Map Metadata Automation Disparity You are here Ideal State Ingestion Integration Info Value Archive