Get the Most Out of Big Data Analytics
The amount and type of data related companies continues to increase each year. This is what is known as big data. It includes everything from social media posts, images and audio to sensor data, transaction records and video. The amount of data collected keeps on increasing and it is up to companies to find better ways of collecting the data and analyzing it properly. A study by the IDC found that data is growing by up to 40% each year and this will continue to grow for years to come.
The challenges that most companies face today are on how to efficiently as well as cost effectively collect and store the fast growing data. The real benefit of big data lies in being able to analyze the collected data. It is only by analyzing the data that the company can be able to use that information to improve their product quality, boost customer service, speed decision making and optimize the business processes. This post focuses on a few tips on getting the most out of big data analytics.
Have a business problem in mind
For you to be able to analyze the data you collect, you must be looking for a solution to a specific problem. This is why you should always start with by having a business problem in mind.
There are so many tools out there that you can use for data analytics. These tools actually make the process fun for the analytics. However, this could all be a waste of time and money if the results don’t translate into something that can help solve a real-world company problem.
The first step should always be to identify the projects that will be promising and practical. You should then find the different types of problems that big data analytics can help solve for the organization. Simply put, before you get started with big data analysis, you have to identify the kind of problem/ challenge that can be addressed using the data that you have already collected. You then have to make sure that the data that is analyzed is accurate, current and offers real insight.
Consider how you will deploy the insights
In order for you to achieve real value from the data in your database, you should be able to operationalize results of your analysis. This may sound obvious to you now but the truth is there are so many projects that are left gathering dust simply because it proved too hard to leverage findings whether they could have provided value. The cost to a company in this case can be immense.
The proper selection of data is very critical. You have to make wise decisions and consider the fact that what may look wonderful on paper may not be available or can be too expensive to obtain. The industry regulations may also have an impact on how the data collected can be used and where it can be collected from.
The analytics development team has to be very careful on how their models will be published and used by the customer service, marketing, product development and/or operations teams. Models that call for manually intensive data processing steps can be problematic in the implantation stage. It is possible for the organization to work with an experienced remote database expert to help with planning and analytics.
Technology advances make it possible for organizations to avoid most of the problems faced in big data analytics. However, you still need a streamlined analytical method. This will reduce time to operational value as well as make analytic results easier to share and reuse for various purposes. Having a mechanism in place to enforce the best practices in model management will enable you to avoid development as well as implementation delays.
Leverage on analytic innovation
Innovations in the realm of big data processing and analytics continue to transform how companies get value from customer data. There is a shift from approaches that supply periodic snapshots in the form of dashboards and descriptive reports to shifts in systems that analyze incoming data in real time and continuously.
There are many big data infrastructure and tools that make it easier for companies to apply Machine Learning techniques to help explore huge datasets, which include a variety of structured as well as unstructured data. The balance of the techniques with human analytics as well as domain expertise lifts the business performance and improves the ability of the company to learn at a fast pace from the data-driven experiments.
Embrace analytic diversity
To be able to get multiple types of analytic models that work together in efficient development environments and robust production environment, you will need an infrastructure that is flexible and one that embraces diversity. The key requirements include the ability to operationalize models that are authored by a range of tools by supporting extensible libraries, standards and web services like Predictive Modeling Markup Language.
It is also crucial to build a culture of documentation as well as control while at the same time leveraging on all the modern tools. The production use of the analytics requires discipline and control while at the same time finding better ways to not suppress creativity.
Leverage on cloud services
The best thing about the current advancements in technology is that when it comes to big data, you no longer need to make huge investments in infrastructure and specialized skills. You can now leverage on cloud services so that you get a dedicated third party securely handle the system and service needs on your behalf. You only pay for the capacity and the services that you need. This option is less costly, quicker and improves cross-functional visibility as well as coordination mostly when compared with the traditional method of one-to-one system integration.
As a parting note, you must learn to balance automation with expertise. You can never replace the human interface when it comes to big data analytics. You will still need the human part to evaluate the system. Hire a competent team to help manage the system.
By Sujain Thomas
Sujain works as a remote database expert offering big data services to companies in different kinds of businesses. She takes pride simplifying big data management.
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