Big data analytics is a trending practice that many companies are adopting. Before jumping in and buying big data tools, though, organizations should first get to know the landscape.
In essence, big data analytics tools are software products that support predictive and prescriptive analytics applications running on big data computing platforms typically, parallel processing systems based on clusters of commodity servers, scalable distributed storage and technologies such as Hadoop and NoSQL databases..
In addition, big data analytics tools provide the framework for using data mining techniques to analyze data, discover patterns, propose analytical models to recognize and react to identified patterns, and then enhance the performance of business processes by embedding the analytical models within the corresponding operational applications.
Powering analytics: Inside big data and advanced analytics tools
Big data analytics yields a long list of vendors. However, many of these vendors provide big data platforms and tools that support the analytics process for example, data integration, data preparation and other types of data management software. We focus on tools that meet the following criteria:
- They provide the analyst with advanced analytics algorithms and models.
- They’re engineered to run on big data platforms such as Hadoop or specialty high-performance analytics systems.
- They’re easily adaptable to use structured and unstructured data from multiple sources.
- Their performance is capable of scaling as more data is incorporated into analytical models.
- Their analytical models can be or already are integrated with data visualization and presentation tools.
- They can easily be integrated with other technologies.
Big data and advanced analytics tools:
While some individuals in the organization are looking to explore and devise new predictive models, others look to embed these models within their business processes, and still others will want to understand the overall impact that these tools will have on the business. In other words, organizations that are adopting big data analytics need to accommodate a variety of user types, such as:
The data scientist, who likely performs more complex analyses involving more complex data types and is familiar with how underlying models are designed and implemented to assess inherent dependencies or biases.
The business analyst, who is likely a more casual user looking to use the tools for proactive data discovery or visualization of existing information, as well as some predictive analytics.
The business manager, who is looking to understand the models and conclusions.
IT developers, who support all the prior categories of users.
How Applications of Big Data Drive Industries:
Generally, most organizations have several goals for adopting big data projects. While the primary goal for most organizations is to enhance customer experience, other goals include cost reduction, better targeted marketing and making existing processes more efficient. In recent times, data breaches have also made enhanced security an important goal that big data projects seek to incorporate. More importantly however, where do you stand when it comes to big data? You will very likely find that you are either:
- Trying to decide whether there is true value in big data or not
- Evaluating the size of the market opportunity
- Developing new services and products that will utilize big data
- Already utilizing big data solutions Repositioning existing services and products to utilize big data, or
- Already utilizing big data solutions.
With this in mind, having a bird’s eye view of big data and its application in different industries will help you better appreciate what your role is or what it is likely to be in the future, in your industry or across different industries. With this in mind, having a bird’s eye view of big data and its application in different industries will help you better appreciate what your role is or what it is likely to be in the future, in your industry or across different industries.
In this article, I shall examine 10 industry verticals that are using big data, industry-specific challenges that these industries face, and how big data solves these challenges.
Having gone through 10 industry verticals including how big data plays a role in these industries, here are a few key takeaways:
- There is substantial real spending around big data
- To capitalize on big data opportunities, you need to Familiarize yourself with and Understand where spending is occurring
- Match market needs with your own capabilities and solutions
- Vertical industry expertise is key to utilizing big data effectively and efficiently
- If there’s anything you’d like to add, explore, or know, do feel free to comment below.