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Hortonworks Big Data Scorecard Survey

Sponsorship

1. Vision & Strategy

My organization has a very limited big data vision and strategy.
Key leaders in my organization are talking about big data, but focused on specific business problems instead of an enterprise-wide vision.
An enterprise-wide vision and strategy is taking shape in a formal big data roadmap.
Executive and management teams are aligned on an enterprise big data strategy.

2. Funding

Funding for big data programs is unbudgeted or non-existent in my organization
Some big data projects are getting funded in my organization but usually on a project-level and/or often from IT budgets.
Big data programs are a considered in cyclical budgeting processes at the executive or line-of-business levels.
Big data programs are budgeted and funded in executive and business unit levels.

3. Advocacy

Big data program advocacy is limited to IT or other isolated business groups in my organization.
Big data has at least one executive-level sponsor in my organization, most likely a CTO or CIO.
Big data programs are advocated by multiple senior-level executives.
Executive and business managment in my organizaiton are aligned on the value of data as currency, and actively advocate for its use in key business processes.

4. Business Case

The business case for big data in my organizaiton has not been formally established at the business level or enterprise level.
Pilot project(s) in at least one business group have resulted in local business cases for big data investment.
Multiple pilot projects in more than one business unit have resulted in business cases for big data investment.
My business has realized at least one new revenue stream or business model from big data analytics.

Data & Analytics

1. Data Collection

My organization works primarily with structured data, and must manually collect much of the data we store.
We are ingesting some unstructured data from new sources that didn't exist a few years ago.
The collection of both structured and unstructured data is automated in my organization.
My organization routinely seeks out new data sources of all types.

2. Data Storage

My organization has a limited storage capacity, and we store data in various file formats if we store it at all.
There's an emerging understanding of the importance of storing all our enterprise data, but some data is routinely discarded.
My organization has a unified information architecture and we rarely discard data.
My organization has created a "data lake" or shared data service that pools our enterprise data in a unified architecture.

3. Data Processing

Our data processing typically involves structured data in manual processes.
We lack a common metadata/naming structure across the enterprise, but metadata standards are emerging at the business level.
Metatdata/naming conventions are aligned to a unified enterprise architecture, and consisently applied.
Our organization has a data processing engine that ingests and transforms data to align to our enterprise information architecture.

4. Data Analysis

My organization's data analysis activities are focused primarily on reporting key business metrics, usually measuring performance.
We dabble in advanced analytics, and those projects tend to have a long time to value.
My organization has a predictive analytics engine and/or the ability to perform real-time analysis.
Our data quality, timeliness, and value is routinely and formally evaluated and optimized.

Technology & Infrastructure

1. Hosting Strategy

We primarily host our data storage and analytic applications on premise.
We are exploring or migrating data storage and applications to the cloud, in addition to our core on-premise hosting.
We seek (or have adopted) best-in-class hybrid hosting infrastructure, with some cloud and some on-premise.
We have optimized our hybrid hosting solution to deliver unified access and consistent speed and dependability.

2. Functionality

We have a traditional data warehouse focused on storage of structured data.
We have at least one big data proof of concept project such as Hadoop.
We have adopted a Tier-2 production class Hadoop cluster and are capable of supporting multiple workloads.
We have a Tier-1 production class Hadoop cluster capable of handling multiple data types from multiple sources.

3. Analytic Tools

Our organization has basic analytic tooling to support canned reporting.
We have adopted analytic tooling to support project-specific objectives.
Centralized resources are made available to business groups seeking fit-for-purpose tools.
Centralized tooling is administered by a big data group supporting various business programs.

4. Integration

Our data infrastructure requires constant maintenance and tuning to support basic storage and access needs.
We see some integration between analytic tools deployed across the organization.
Many of the data tools and resources are integrated across the organization.
Our data infrastructure is centralized and tightly integrated.

Organization & Skills

1. Analytic & Development Skills

Our big data skills tend to be located among analysts and other technologists at the business level.
We have in-house talent to support data collection and storage.
Our oranization is investing in big data skills that go beyond collection and storage to include data mining and other forms of advanced analytics.
My organization provides training and support for data-related programs across the company.

2. In-house or Outsourced

We are outsourcing most of our work related to big data planning and readiness.
We combine some in-house big data skills with outsourced support for proof of concept projects.
We have core Hadoop and NoSQL skills in house, but rely on external resources for many advanced data capabilities.
We have built an in-house organization with most of the skills required by our big data roadmap.

3. Leadership Model

We currently do not have a centralized analytics group.
We are talking about the potential value of centralizing data and analytics in our organization.
We have created a central data/analytics group with a cross-functional service focus.
We have a centralized big data center of excellence coordinating and supporting decentralized resources.

4. Cross-functional practices

Most of our data work is done at the departmental level, with little conversation between functional groups.
Business groups routinely communicate about data programs, and share data resources.
Our Hadoop experts and data warehouse experts routinely collaborate in support of cross-functional programs.
We have created a big data steering committee to ensure organizationl alignment to our roadmap and resources.

Process Management

1. Planning & Budgeting

My company has no formal processes for planning big data programs and investments.
Planning for big data programs and investments happens at the business level only.
Executive and management processes are aligned for annual review and planning around big data investments.
Data is deeply integrated into planning and investment for every business unit, with widely embraced standards for collaboration and workflows.

2. Operations, Security & Governance

My company has basic data security processes in place but undefined processes for data collection and/or access.
Data operations and governance are being discussed and improved with collaboration between IT and some business units.
An enterprise-wide policy and protocol for data collection and access is in place.
My company adheres to enterprise-grade standards for security, back-up, disaster recovery, and access across all public and private cloud data infrastructure.

3. Program Measurement

There is little or no evaluation of the quality or effectiveness my company's data-related programs.
My company is beginning to see decision support processes emerge from big data pilot projects.
We conduct routine cost/benefit analyses and monitor decision processes and outcomes using data.
Performance measurement standards are defined at a company level and applied routinely by centralized leadership.

4. Investment focus

Investment in big data programs are made on an ad hoc basis with little or no ROI analysis after the fact.
Big data investment is focused primarily on data warehouse optimization and justified primarily on the basis of storage and processing efficiency.
Investment is targeting analytic capabilities directed at discovering and developing new value streams.
Investment in big data is targeting the refinement of new business models that emerged from advanced analytics.

Additional Info

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