Big Data Integration and Analytics: What CIOs Need to Know to Succeed
A disconcerting fact about big data adoption is that 55% of the projects never reach the completion stage!
Even when those projects see completion, a large percentage of them fail to realize the benefits they were expected to produce.
Multiple estimates and researches indicate that nearly 75% of all medium to large scale enterprises, worldwide, either have or are planning a big data strategy. Big data has stopped being a ‘mission-critical’ endeavor and has transformed into a ‘critical’ factor for survival. Therefore, it is all the more paramount for businesses to ensure the success of their big data strategy.
The most rational way of achieving this would be to detect and analyze the factors contributing to the high failure rates of big data adoptions and then address them. That’s exactly what has been done here. Here is a list of things that CIOs must know to ensure the success of their big data strategy.
How to Succeed in Integrating Big Data and Analytics
1. Decide Key Metrics and Use Cases
Big data offers tremendous benefits for businesses. But, focusing on too many benefits can easily become overwhelming, both for the back-end and front-end resources. Not to mention the significant costs involved. Therefore, it makes better sense for businesses to embark on a big data strategy with a specific business case at hand. This should be followed by the brainstorming and finalizing of various key metrics relevant for the business case. Those key metrics should help business arrive at reliable insights, which can then be used for decision-making. More importantly, the business case should result in tangible improvement in revenue or reduction of costs.
Once the enterprise has a proof of concept, they can proceed to develop other business cases and finalize the metrics relevant for them. This way, businesses can extract maximum advantage from their big data strategy without overwhelming their resources, which also increases the chances of the success of the project.
2. Seamless Integration of Data
A few years ago, ‘breaking down the silos’ was a cool buzzword used by visionary business leaders. Today, it is imperative to the success of a business. Big data endeavors will be successful when enterprise data is obtained from all the departments, and other sources available to the business. So, long term enterprise big data strategies should encompass every department, every aspect, and every area of enterprise operations. This can be made possible by developing an organization-wide big data collection, integration, analysis, and data-driven decision-making process. This brings us to the next point.
3. Automate Data Onboarding
Organizations typically either rely on intra-organizational socialization or make cultural changes to break down the silos that exist between the various departments. Both of these choices court intense resistance from within the organization, as they mean major changes to how the employees work. This can hinder the big data adoption or derail the entire project altogether. That’s why a better option would be to use a third-party big data tool for sourcing data automatically from all the relevant departments and data sources. This would set the foundation for interaction between different departments and will make the drive towards complete organizational integration smoother.
4. Streamlining of Back-end
A bulk of the time and resources deployed into big data projects are consumed by the back-end activities like bringing together multiple technologies, cleaning of data, and others. More time is spent on getting the data ready for analysis than on the analysis itself. Enterprises can save abundant amounts of time by outsourcing, streamlining, or automating data cleaning and preparing process. This frees up precious IT resources, who can now be deployed into managing the front-end aspects of big data, which add tangible value to the businesses. They can develop applications for analyzing the data, create dashboards, develop scripts for analysis, and even perform analysis to obtain insights for various departments.
5. Minimize Coding for the Actual Users
One of the most sought after skills among data scientists is the ability to code in multiple languages related to data science, big data, machine learning, and related fields. Therefore, most of the data scientists can use their coding skills to splice, manipulate, and analyze big data. However, that drastically brings down their productivity. Therefore, CIOs should implement big data processes that minimize coding for the end users like data scientists.
Visual tools, drag and drop features, simple check boxes, buttons, and other UI elements can be implemented at the application level for the ease of data scientists. A number of big data integration tools offer zero coding integration support, and enterprises can use them to deliver a superior big data integration to their data scientists.
Big Data Integration With Pentaho Data Integration
Pentaho Data Integration from Hitachi Data Systems is a feature-rich, data integration tool that enables enterprises to collect, clean, and integrate data seamlessly from multiple sources and technologies. It comes with innovative features that minimize coding and allows end users to use data effortlessly, thereby dramatically bringing down the time spent on preparing and integrating it.
Pentaho Data Integration offers a plethora of additional features and functionalities that boost the chances of success of your organization’s big data strategy. To know more about this powerful tool and get a better understanding of how it can revolutionize your big data adoption, get in touch with AsiaPac here.
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