DA AND DATA VIRTUALIZATION

Data Virtualization (DV), while not a new concept, is still quite misunderstood.

Data virtualization allows an organization to make its enterprise data easily available to business users. From a more technical standpoint, data virtualization is a form of middleware that leverages high-performance software and an advanced computing architecture to integrate and deliver data from multiple, disparate sources in a loosely-coupled, logically-federated manner. It differs from the traditional ETL/Data warehouse solutions by leaving the data in place – in the originating data sources – and extracting it as and when needed by the consuming applications. With the growth of data and complexity of IT infrastructures over the past decade or so, data virtualization is becoming ever-more important. It now can provide numerous benefits to enterprises in many different arenas. Some of those benefits include:

• Gaining more business insights by leveraging all your data –

Empowering people with instant access to all the data they want, the way they want it.

• Responding faster to your ever changing analytics and BI – Five to ten times faster time to solution than traditional data integration.

• More cost effective than data replication and consolidation – Reduces unnecessary copying of data. Data virtualization’s streamlined approach reduces complexity and saves money.

Yet, even with those benefits listed, numerous enterprises are not moving forward with virtualization technologies. In the first analysis section of this paper [Section 4–Addressing Enterprise Needs], the ambiguous nature of data virtualization in the modern enterprise was demonstrated clearly. Figure 9 had respondents answer about which elements of Data Management should or should not be included in their data architectures. DV was in first place in what “should be included” at 58.9%, and first place in “what should not be included” at 18.6%.

Clearly, it remains one of the least-understood and least-utilized of the all the elements discussed in this paper.

Therefore, to help provide further clarity to this often misunderstood architectural technology, the survey asked five questions about the utilization of data virtualization at the enterprise level.

Survey Results and Statistics

The initial DV question of the survey asked what statement best represents the respondent’s organizational view in regards to DV. The top two answers really establish where DV is within the Data Management industry and why more education is necessary. The top two answers [Figure 23] were:

• We are not very familiar with DV: 32.3%.

• We know what DV is, but not considering seriously at this time: 28.6%.

When viewed in terms of the next two questions [Figures 24 and 25], the most prevalent path to DV becomes clearer. Each of the following questions was rated on a 1-5 scale, with 5 being the most likely to use or best option. The results will be shown in two separate formats, as a percentage and as a rating average. The best use of DV for the respondents’ organizations is as an Agile BI Enabler (19.8%/3.03 rating) and for Access to New Data Sources (11.8%/2.96 rating):

Figure 23 (133 respondents)

0.0%   5.0%   10.0%   15.0%   20.0%   25.0%   30.0%   35.0%  

We  are  using  or  ac4vely  pursuing  adop4on  of   Data  Virtualiza4on  technologies   We  know  what  DV  is,  and  are  keen  to  learn  

more  about  its  benefits  and  uses   We  know  what  DV  is,  but  not  considering  

seriously  at  this  4me  

We  are  not  very  familiar  with  DV  

Which  of  the  following  best  represents  your  company’s  view  of  Data   Virtualiza;on?  

The main factors or “pain points” that are pushing the respondents’ organizations towards DV integration into their existing systems are:

• Real-Time or On Demand access to information: 26.9%/3.21

• Reduce Replication of Data/Silos: 22.6%/3.18

• Time to Market/Agility: 24.3%/3.27

Figure 24 (123 respondents)

0   20   40   60   80   100   120   140  

Enterprise  Strategy    –  Implement    at  enterprise  / broad  level  to  create  Data  Services  /  IaaS  across  

analy?cal  and  opera?onal  uses  

Agile  BI  Enabler    –  Component  to  add  agility  to   BI,  EDW,  MDM  ini?a?ves  

Single  View    Applica?ons    -­‐  Support  Portal,  Call   Center  etc.  ini?a?ves  

Managed  Migra?on  –  Abstrac?on  Layer  for   managed  migra?on,  mergers,  acquisi?ons   Access  New  Data  Sources  –  Integrate   Unstructured,  Semi-­‐Structured,  Web  ,  Cloud  data  

more  easily  

How  and  where  would  you  use  Data  Virtualiza4on?    (rate  each  on  scale  of  1-­‐5,  5   being  most  likely  to  use)  

1  (Least  Likely  to  Use)   2   3   4   5  (Most  Likely  to  Use)  

When asked to consider the most preferred approach to DV, respondents were given three different choices., “Best of Breed Data Virtualization Platform” had the highest percentage at 16.7% though the highest rating average was for “BI Tools with Integrated Federation Capability” at 2.90 [Figure 26]:

Figure 25 (114 respondents)

0   20   40   60   80   100   120  

Time  to  Market  /  Agility   Lower  Integra;on  Costs   Real-­‐;me  or  On  Demand  access  to  informa;on   Reduce  Replica;on  of  Data  /  Silos   Complexity  /  Heterogeneity  –  Access  XML,  Big  

Data,  NoSQL,  Unstructured,  Web   Abstrac;on  -­‐  Unified  Business  Views  of  Data   Data  Services  Delivery  –  Secure  enterprise  data  

sharing  

What  are  the  main  factors  or  pain  points  with  current  integra3on  approach  that   is  driving  you  to  consider  Data  Virtualiza3on  (Rate  1-­‐5)  

1  (Least  Important)   2   3   4   5  (Most  Important)  

The final question for this section asked respondents to rank their criteria for the selection of a DV tool. They were given seven separate choices, with a possible ranking of 1-4 (4 being the best). The top three choices (they could select more than one) were [Figure 27]:

• Pricing/Total Cost of Ownership: 39.6%/3.12

• Performance, Caching, Scalability Features: 31.4%/2.84

• Ability to Handle Structured and Unstructured Data: 28%/2.49

Figure 26 (102 respondents)

0   20   40   60   80   100   120  

Extension  to  Incumbent  Data  Integra9on   Vendors’  Products  

BI  Tools  with  integrated  Federa9on  Capability   Best  of  Breed  Data  Virtualiza9on  PlaIorm  

Which  approach  to  Data  Virtualiza0on  do  you  support  more  (Rate  on  1-­‐5  scale)  

1   2   3   4   5  

Analysis of Results

The results of these questions suggest that it is still early in the consideration and adoption of data virtualization as part of an enterprise data strategy.

For example, the scores of the different choices for Figure 24 about ways of using data virtualization were generally even. Yet, the relatively high score in Figure 24 for using data virtualization for accessing new data sources and integrating unstructured and semi-structured data, cloud data, and web data somewhat contrasts with the relatively low score for Figure 25 (about pain points and drivers for data virtualization) “Complexity/Heterogeneity–Access XML, Big Data, NoSQL, Unstructured, Web.” One might infer that accessing new (and “big”) data sources is less of a priority, and that in fact providing faster access to data in the data warehouse (“… add agility to BI, EDW” and “real-time or On-Demand access to information”) is the more critical driver for introducing data virtualization into the enterprise today.

Figure 27 (104 respondents)

0   20   40   60   80   100   120  

Source  Breadth    –  Access  to  most  number  of   sources  

Specify  your  most  important  sources  (other  than   databases):  

-­‐  Ability  to  handle  structured  and  unstructured   data  

-­‐  Modeling,  TransformaFon,  Governance   CapabiliFes  

-­‐  Performance,  Caching,  Scalability  Features   -­‐  Data  Services  Publishing  OpFons  (SQL,  Web  

Services,  JSON,  Portlets)  

-­‐  Pricing  /  Total  Cost  of  Ownership  

Rank  the  Criteria  for  selec1on  of  DV  tool  (Rate  on  1-­‐4  scale)  

1  (Low)   2   3   4  (High)