Following on my Data Integration is Old News post, we are advising organizations to move to a more strategic way of managing their data, an approach that we refer to as Information Management. Going forward, you can expect to hear much more about this topic from SAS, but I thought I'd start the discussion with this series of blog posts:

1. Business and technical factors that are driving a need for a strategic Information Management approach - this post
2. Evolution from reactive Data Integration to "managed" Data Management to strategic Information Management
3. SAS Information Management defined

So let's jump into the business and technical factors driving the need for strategic Information Management...

The rate of change in today’s business climate is staggering – competitive and economic pressures are constantly increasing. The rate, diversity and complexity of data that is coming at organizations is unrelenting. The need to transform this data to information and insight that drives business success is more important than ever. The need to view information as a strategic asset, that allows organizations to make accurate decisions within the decision window of the competition is key to success.  It’s not about the data or how much you can process, it’s about capitalizing on all the data assets that are available to organization to provide insight and drive fast and accurate decisions.

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Gartner sums this up in recent research:

"Leveraging information will continue to fuel business success. But the growth in information volume, velocity, variety and complexity and the new information use cases makes information management infinitely more complex than it has been in the past. In addition to the new sources and the increased demand for multiple context delivery, shareability and reuse, practically all information assets must be available for delivery through varied, multiple and concurrent channels and mobile devices. To deal with these new demands, the IT organization needs to dramatically modernize its IT systems, transforming outdated data management infrastructure and replacing it with a more up-to-date and superior information environment able to support an entirely new set of requirements."

Source: Gartner, Information Management in the 21st Century, Regina Casonato, Anne Lapkin, Mark A. Beyer, Yvonne Genovese, Ted Friedman September 2, 2011

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SAS considers the following factors since they represent the diversity or drivers as well as breadth of scope that call for a comprehensive information management approach:

Let's take a look at each of these in more detail:

Data Diversity – the information management strategy should accommodate the diversity of data, including the following attributes:

• Big data: Volume, velocity, variety – big data is relative, but all organizations need to think about the entirety of the data is at their disposal and how the requirements for storage, scale, processing, etc., will multiply in the future.
• Big text / unstructured data – in many cases, the big data phenomena is being driven by text or unstructured data and the need to mine value out of that text is critical for both analytical and operational scnearios. It is critical that information management strategies effectively accommodate text and unstructured data and this highlights the need for a combined analytics and data management capability.
• In-flight (perishable), data at rest – the ability to support data that is in-flight is becoming increasingly important given the large volumes of data that people are faced with and the increased amount of streaming data sources.
• Internal, External – it’s not just about internal data, and all internal data is not “on premise”. It’s also about partner data and other 3^rd party data sources that need to be leveraged and managed effectively.
• Text / Unstructured / Semi-structured – although all data and data source types need to be accommodated in a comprehensive strategy, data types that are outside of the traditional legacy or relational structure are worth noting. This is where many organizations struggle, they have a decent handle on transactional or relational data, but text, unstructured, semi-structured (whatever you want to call it) is typically a challenge.
• All forms of information assets – addressing data diversity needs to be expanded beyond traditional sources of data. For example, analytical models are information assets that need to be leveraged and managed effectively.

 

Consumption Diversity – the information management strategy needs to support an increasingly diverse set of consumption “devices” in a continuous and concurrent fashion:

• Support for multiple form factors and form factor versions including PCs, smart phones, tablets and industrial devices (medical, network, etc.)
• Support for user based interaction as well, automated and machine to machine support using a service based approach.

 

Use Case Diversity – the information management strategy should support all relevant use cases for an organization, including:

• Analytical prep, data migrations, single view, enterprise application integration, B2B integration, etc.

 

Application Diversity – the information management strategy should support all types of applications that span operational and analytical:

• Analytics – BI, Data Mining, Text Analytics, Forecasting, Optimization, etc.
• Operational / Transactional – ERP, CRM, HR, Supply Chain, etc.

 

Temporal Diversity – the information management strategy should support the various temporal requirements relating to data – from batch to extremely low-latency (real-time or near-real time):

• Batch – for information processing that is suited to batch requirements, the processing must be supported in the appropriate batch window. In many cases, IT is being squeezed on both ends – batch windows are shrinking (in order to support 24x7x365 processing driven by international business, on-line commerce, etc.) while the amount of data and the processing or analytical requirements are expanding.
• Real-time / Near-real-time – there is an increased need to support processing in real-time via the introduction of rich information and analytic services.

 

Deployment Diversity & Scope – the information management strategy needs to support multiple deployment approaches:

• The information management strategy should effectively leverage multiple deployment or infrastructure options including:Cloud (public, private, hybrid) as well as various “as a Service types” like SaaS, PaaS, Analytic Results as a Service /On-premise /Appliance
• The information strategy should take an expansive view of the capabilities and services that are needed to deploy and manage production information systems and the support necessary to support development, test, production cycle associated with operational applications or the iterative sandbox environment and production cycle associated with analytical applications.

 

Architecture Approach – the information management strategy should support a robust architecture that supports a broad range of technical requirements:

• Enterprise Architecture approach – although not related to diversity, it’s important that an enterprise architecture approach is used to formulate the overall information strategy.
• The architecture approach should accommodate the ability to manage the analytical assets or models that are leveraged in analytical scenarios.
• Centralized or distributed – based on the needs of the organization (which will likely change), the information management strategy should support a centralized or distributed approach.
• Service-based SOA – supporting the diverse set of information and analytic requirements typically calls for a service-based approach.

 

Persona / Role Diversity – the information management strategy should support all of the different roles that are associated with managing information, including:

• Business – support for the key business stakeholders, both business analysts and end users
• IT – support for the various roles that work with data in IT, including architects, DBAs
• Data Stewards – support for those that are responsible for managing data governance
• Data Scientists (data analyst, statistician, etc.) – support for those that are responsible for managing analytics

 

Hopefully this post will provide you with a list of considerations as you move forward with your information management strategy. As you tackle your tactical data related projects, start thinking about a longer term, more strategic information management approach.

My next post will explore how Information Management is a natural progression from a tactical data integration approach.