This is a useful framework. Big step from 3 to 4 - building the data model to publishing new insights. Need to drill down here.
Getting valuable insights from all this new data is tricky. Distinguishing the good useful data from the worthless data is a slow tedious task. Can this be automated?
Automation is key. Solution is Master Data Management (MDM), See: http://www.rosebt.com/master-data-management-mdm.html
See Trends in Business Analytics - http://www.rosebt.com/trends-in-business-analytics.html
Automated processes is crucial for making sense of ever-increasing amounts of data. Master data management and data quality is very important. Data quality tools if used correctly as part of a master data plan can help automate the data sifting process.
See Data Quality Tools Magic Quadrant 2011:
DataFlux is the top ranked visionary leader data quality tool.
The DataFlux Data Management Platform provides organizations with the ability to plan and complete data integration, data quality and master data management (MDM) projects – all from a single interface. DataFlux has combined its industry-leading capabilities into a unified development and delivery environment, helping business and IT to work together on critical aspects of data management.
With a single suite of products, you can transform your data into a trusted business asset, driving data governance, master data management, data quality, data integration or any other data improvement initiative.
Check out the DataFlux Data Management Platform @ http://www.dataflux.com/Products/Products.aspx
In general, automation is particularly relevant when you do not know what you are looking for as opposed to looking for something that you already expect.
For example, discovering exceptions to relationships (business data rules) that have been pre-defined is one thing but looking for similar exceptions to rules that you do not actually know about is of an order of magnitude more complex and will therefore benefit from increased automation.
See Big Data Supply Chains: Volume, Variety, Velocity and Veracity -
See also Data Volume, Variety, Velocity and Veracity - http://www.rosebt.com/1/post/2011/11/data-volume-variety-velocity-and-veracity.html
Data integration strategy is critical - pulling together data from multiple source systems and consolidating in data warehouses for analysis. Yet data integration can go bad and blow the entire data analytics plan if it isn't well designed and properly executed.
For example, bad timing can spoil everything: Data needs to be loaded into a data warehouse in time to be used for planned purposes.
Thus, fully understanding an organization's requirements, particularly when users need real- or near-real-time access to data, is crucial.
Great comments. Dan hit the nail by asking how to go from data collection to analysis to useful insights that help make better decisions.
To obtain useful, actionable insights I suggest hiring a team of data scientists to work with IT and managers. Data scientists provide professional data, analytics, predictive modeling, and data visualization counsel. They extract information from large datasets and present something of value.
They can help you understand, interpret and analyze massive data sets and apply exploratory or predictive analytics to solve business problems. A solid team will creatively apply data science to offer alternative approaches and solutions.
Critically, they help display and visualize raw information so its value can be understood by business decision makers.
Simply put, you are:
Disseminating information and insights
We can help you architect a data strategy, select the right tech, integrate, and implement.
The job of analyzing and disseminating info is the ballywick of data science.
Data scientists apply advanced analytics, predictive modeling, and data visualization skills to different business situations.
See Three Stages of Analytics Adoption - http://www.rosebt.com/1/post/2012/02/three-stages-of-analytics-adoption.html
Simplifying helps to understand process. I get the data science issue.
Yet most of the data in its originally received form is of low value. The process of turning into high value data that can be used for positive business actions is mysterious and scary.
It is scary and hard and outside the comfort zone. But the rewards are big:
1. Improving the decision-making process.
2. Speeding up the decision-making process.
3. Better alignment of resources with strategies.
4. Realizing cost efficiencies.
5. Responding to user needs for availability of data on a timely basis.
By embracing an analytical approach, companies identify their most profitable customers, accelerate product innovation, optimize supply chains and pricing, and identify the true drivers of financial performance.
IBM’s comprehensive, unified portfolio of business analytics software (Cognos, SPSS, OpenPages and Algorithmics) provides a host of capabilities that help your organization achieve your objectives and exceed expectations. Based on open standards, IBM business analytics products can be used independently, in combination with each other, and as part of broader solutions to key business challenges.
Poll: Best Business Intelligence Platforms - http://www.rosebt.com/business-intelligence-data-warehouse.html#pd_a_5405697
Data Warehouse Database Management Systems Magic Quadrant 2011
Top Four Benefits of Analytics
Analytics is about having the right information and insight to create better business outcomes.
Business analytics means leaders know where to find the new revenue opportunities and which product or service offerings are most likely to address the market requirement. It means the ability to can quickly access the right data points to evaluate key performance and revenue indicators in building successful growth strategies. And, it means recognizing regulatory, reputational, and operational risks before they become realities.
1) Having the knowledge you need: Analytics delivers insightful information in context so decision makers have the right information, where, when and how you need it.
2) Making better, faster decisions: Analytics provides decision makers throughout the organization with the interactive, self-service environment needed for exploration and analysis.
3) Optimizing business performance: Analytics enables decision makers to easily measure and monitor financial and operational business performance, analyze results, predict outcomes and
plan for better business results.
4) Uncover new business opportunities: Analytics delivers new insights that help the organization maximize customer and product profitability, minimize customer churn, detect fraud and increase
Big Data Solution
Unlock business insights from all your structured and unstructured data, including large volumes of data not previously activated, with Microsoft’s Big Data solution. Microsoft’s end-to-end roadmap for Big Data embraces Apache Hadoop™ by distributing enterprise class Hadoop based solutions on both Windows Server and Windows Azure. Our solution is also integrated into the Microsoft BI tools such as SQL Server Analysis Services, Reporting Services and even PowerPivot and Excel. This enables you to do BI on all your data, including those in Hadoop.
Big Data Analytics Maturity Model
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