Exploring the concept of Data-as-a-service and "clean data"

What does BDNA do?

BDNA's Data-as-a-Service (DaaS) is quite simple – we take data from enterprise systems, clean it, mash it up with market intelligence to provide insights that directly lead to action (ed: AKA actionable insights). Adopting an end-to-end approach means cutting through many layers of complexity and overhead to provide accurate insights at blazing speed, fastest means to identify and reclaim wasteful spend, greatly reduced risk to your key initiatives, reclaim IT innovation budget by saving costs

The following picture shows how BDNA DaaS is applicable for customers that have adopted the ServiceNow SaaS platform. The same applies to customers with other types of challenges.

What is Data as a Service?

Data as a Service (DaaS) is an industrialized approach to solving data related challenges such as cleansing, filtering, aggregation and enriching data. The notion of DaaS is that these services can happen in a centralized place thereby providing a more efficient and effective means of solving problems with data.

How is BDNA different than its competitors?

BDNA is different than competitors in two ways: Firstly, we cover the entire breadth of the IT estate (hardware, software, applications, mobile devices, etc.) and secondly, we can automatically mash up "market intelligence" with "enterprise data".

Today, other DaaS vendors only provide market intelligence for a specific aspect e.g. Pricing. It is up to the enterprise to determine how to use this intelligence within the enterprise. BDNA not only provides the market intelligence but also the means to automatically mash it up with enterprise data. BDNA is the only vendor that provides both the market intelligence and mashes it up with enterprise data.

What is clean data?

Clean Data is data that is definitively the best quality of data you can achieve. It is not just "best effort" or "best reconciled" but the result of mashing up all your data sources with exhaustive market intelligence. How is clean data different from merely "high quality" data?

Clean data goes beyond data quality efforts like aggregation, de-duplication and reconciliation, to include rich market intelligence. This means clean data is not just the best you can get out of your data but it is the best you can get from all of the data available in the market.

Clean data can provide definitive classification for every single component as known, unknown or irrelevant allowing one to quickly sift out irrelevant data and focus only on the relevant data. In contrast "quality data" often combines the unknown and irrelevant into one bucket thus leading to wasted efforts on processing data that is often irrelevant. With the rising popularity of Big Data the ability to sift our irrelevant data can lead to significant efficiencies.

Describe the importance of clean data enriched with market intelligence in IT projects/audits?

IT projects and audits seek to improve efficiencies and reduce costs and risks. In order to do this effectively decision makers need clean data that is aggregated from different processes, de-duplicated, reconciled, filtered and enriched valuable market intelligence. Here are some examples of IT projects that rely on clean data enriched with market intelligence:

Data Center Transformation: Reducing facility footprint and increasing energy efficiency require evaluating and analyzing multiple products from different vendors on power ratings, dimensions, CPU capacity, cooling etc.

Software License Audits: In order to be prepared for one, the IT organization needs licensing information for all of its software products. Licensing structures vary by vendor and have become extremely complicated and this information requires deep research of specific licensing requirements to be able to effectively defend an audit.

Product Lifecycle Management: Product end-of-life information is a key piece of data that is required to plan product lifecycle management refresh projects.

What type of projects can organizations use clean data for?

Clean data has ubiquitous use. Here are some examples:

  • IT Service Management
  • IT Asset Management
  • CMS/CMDB
  • Data Center Transformation
  • Mobility & Application Modernization
  • Virtualization and Cloud Initiatives
  • IT Cost, Planning and Governance
  • Procurement and Finance
  • License Audits and Compliance
  • Marketing and Sales OS/App Migration

How does an organization currently cleanse their data?

Organizations currently use an ad-hoc approach when a particular initiative demands it. Loosely assembled teams are responsible for data cleansing efforts. Some organizations use multiple general-purpose tools to lash up a solution for clean data, but these are extremely costly to maintain and require lot of care and feed, moreover these seldom deliver the expected results. See image below.

Can you provide a few examples of the results customers have achieved as a result of using BDNA?

The rule of thumb is that 80 per cent of customers achieve average savings of $7M in less than nine months. BDNA was a key component in the largest Windows Migration in history. A consulting firm realized 300-400 per cent ROI in less than a year. A Global 10 banking company achieved 10 to 20 million dollars in savings through license compliance initiative.