How to better develop large data strategy for enterprises

Will large data policies fail? It's time to discuss the problem. Enterprises have just mastered how to integrate ERP (Enterprise resource planning) and other business applications to eliminate the obstacles to efficiency in the business process. Service-oriented architecture, software-services, cloud computing and other modern solutions have played a role in helping enterprises achieve large-scale application integration. But today, organizations are facing a new set of challenges in a large number of data environments. More clearly, it is not a data stream. It is made up of a number of independent data streams, separating the data from each other or as isolated as previous enterprise applications.

It's not a conservative.

Many of these data are not the same as those that businesses use to process data. In a large-scale structured data environment, most of the challenges to data explosion can be solved by scaling, redundancy, and analysis. In the big data age, these challenges are just a few of the issues that businesses must address. The data types collected today are very extensive. Data is transmitted to the database through embedded sensors, RFID chips, boxes and audio-visual supplies, documents and image files, images, and other methods. Social media will change the image of the data. This does not include large data shared between business partners.

The organization no longer describes or prescribes the form to be presented by the data. In fact, trying to do so would greatly reduce the value of the data itself. An enterprise can only predict a certain number of potential plots or reactions. No matter how many check boxes or data files they create, there is always a data overflow. From a competitive standpoint, the consequences of ignoring non-traditional data are devastating. A recent study by the McKinsey Global Institute has shown that big data is the next research direction in the field of innovation, competition and productivity, and that the company will lose hundreds of millions of of dollars if it fails to make full use of existing data.

relational databases provide only partial solutions

The use of tools and techniques to manage unstructured data becomes very difficult because of the large volume and variety of data. Non-relational NoSQL, XML, and critical/numeric data storage can help organizations solve the scalability and accessibility problems of most large data. Solutions such as Hadoop use MapReduce and hive Query Language to provide an enterprise with a starting point for managing large data and obtaining business intelligence. NoSQL database management systems such as MONGODB and Cassandra have implemented Hadoop integration, making it easier for customers to get at least one client interface or overwrite different data streams.

Today the data itself becomes more flexible in the enterprise. Parallel processes and intelligent data are jitterbit such tools, designed to allow data to be transferred from one application to the next, and to ensure the quality of data transmitted. This integration through data types and applications is important for time-sensitive business activities that involve instant analysis. In general, this form of analysis must query the current and historical data to identify new trends. That's why SQL often works again.

SQL, NoSQL, and large data technology

The advent of new data is not a denial of the business data that has been carefully collected and collated over the past decades. Internal enterprise data in the SQL data store can explain the difference between the accuracy and relevance of large and other data. Most organizations find that they still need to maintain the SQL structure for enterprise data to support best business practices. Transforming all data into unstructured format is not integration, it is just convergence processing. At the same time, trying to force structured data into unstructured data is a waste of effort.

From an enterprise perspective, the goal of integration is not to focus on data structures but to focus on organizing. Tools such as the new Oracle Data Integrator try to find the balance by loading and transforming the data Hadoop, so it's easier to analyze with traditional enterprise data. In the analysis process, this approach enables the integration of data from a variety of sources of information and storage, which in this case requires data integration. This eclectic approach makes the original data freer than the initial state, and maintaining this implied value may be more appropriate for the new method of future analysis.

(Responsible editor: The good of the Legacy)