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Prescriptive analytics create recommendations for how workers can make decisions in their jobs. Most managers need some urging to adopt the less familiar predictive and prescriptive analytics, which are typically far more valuable than the descriptive variety.
A few years ago, I did a video explaining the difference between descriptive, predictive, and prescriptive analytics that will come in handy for managers who need a refresher. These are still very important, but now I am increasingly focused on a new type: automated analytics. These analytical decisions are made not by humans, but by computers.
Many common analytical decisions, such as those about issuing credit by banks or insurance policies, are made entirely automatically. Most large and small companies probably store most of their important operational information in relational database management systems RDBMSs , which are built on one or more relations and represented by tables.
These tables are defined by the way the data is stored. The data is stored in database objects called tables — organized in rows and columns.
RDBMSs follow a consistent approach in the way that data is stored and retrieved. To get the most business value from your real-time analysis of unstructured data, you need to understand that data in context with your historical data on customers, products, transactions, and operations.
In other words, you will need to integrate your unstructured data with your traditional operational data. Most big data implementations need to be highly available, so the networks, servers, and physical storage must be resilient and redundant. Resiliency and redundancy are interrelated.
An infrastructure, or a system, is resilient to failure or changes when sufficient redundant resources are in place ready to jump into action. Resiliency helps to eliminate single points of failure in your infrastructure. For example, if only one network connection exists between your business and the Internet, you have no network redundancy, and the infrastructure is not resilient with respect to a network outage.
In large data centers with business continuity requirements, most of the redundancy is in place and can be leveraged to create a big data environment. In new implementations, the designers have the responsibility to map the deployment to the needs of the business based on costs and performance.
Hadoop allows big problems to be decomposed into smaller elements so that analysis can be done quickly and cost effectively. HDFS is a versatile, resilient, clustered approach to managing files in a big data environment.
HDFS is not the final destination for files. MapReduce is a software framework that enables developers to write programs that can process massive amounts of unstructured data in parallel across a distributed group of processors. MapReduce was designed by Google as a way of efficiently executing a set of functions against a large amount of data in batch mode. Integrate data: Establish processes to prepare and normalize data for a myriad of data sources.
This process is often very challenging; resist the temptation to rely on manual methods, and leverage automation and repeatability as much as possible.
Cleanse data: Review the data to ensure its ready for use; that means checking for incomplete or inaccurate data and resolving any data errors that may bias analysis or negatively impact business operations and decision making.
Beware this process can be tedious, and leverage automation options when available. Master data: Organize your data into logical domains that make sense to your business such as customers, products, and services. Furthermore, you can add enrichment data to further paint a clearer picture of your customers, products, and services and their relationships. Secure data: A mix of governance and security allows you to establish security rules and then implement those rules.
First, you must determine how you will manage your sensitive data. Next, you must find and assess the risk of your sensitive data and implement rules via policy and technology.
This process is very important but prone to be under-addressed by those inexperienced in big data management. Based on your hypotheses, find what data exists and how it can be analyzed to create a model that delivers results.
Then determine if the results are beneficial to the business; remember that providing actionable information and processes is the goal. Develop best practices to enhance agility and processes before pushing the solution into the factory. Explore and analyze for business needs: Test out data products to see if they provide a real value for the business; often you just need to try something to see if it works.
Make iterative improvements over time as you learn what works, what doesnt work, and what can be improved. Operationalize the insights: Automate and streamline your processes to create a steady pipeline of actionable insights to business users. Its not enough to have occasional production runs from the big data factory; the factory must be running regularly to be truly productive, meet business service-level agreements SLAs , and achieve the expected ROI.
These processes arent necessarily linear, although they have a general flow with reiteration and loopback as necessary. Really, these processes run as a cycle as data is brought into the system, processed, tested, and then implemented for the business; then the next data project or test is started.
The system will ingest data from data sources, clean, integrate, and manage that data, and then pass it to analytic applications for processing to develop insights and finally to business applications in the form of actionable information, all while applying big data management processes.
Understanding the processes of big data management enables you to better manage environments. The challenge is what can be done to increase their effectiveness and ability to produce results, and in this context I mean working with big data.