Big data developments in 2015 show a significant metamorphosis from how multiple organizations used to store their information just a few years ago. The direction of how data is stored is increasingly shifting toward measuring data agility as opposed to the storage and management of key data sources. The investment and activity capacity of big data projects will be directly related to the impact of an organization's operations when processing and analyzing data, the rate and flexibility in which organizations respond to changing trends among consumers, markets, the competition, as well as to the operation's current status. Organizations can leverage the benefits of big data via data lakes.
Another big data trend organizations will focus on in 2015 revolves around the non-stop, real-time access and processing of data and events in data lakes. This will deliver awareness for organizations when supporting bigger queries and reports when initiating real-time processing and integrating file-based, Hadoop and BI systems into their large-scale processing platforms. There is a high value potential associated with data lakes because they reduce the per-terabyte storage cost to businesses and are easy to upgrade, downsize and adjust.
An excel dashboard is a graphical presentation of data findings and analysis into a condensed, executive summary view. It is especially beneficial to use when clients need to retrieve and read large segments of data from a data lake. Due to its flexibility, organizations can design and implement this dashboard in virtually any way they like without using columns and rows to bring data into their spreadsheets. They also have the advantage of managing and linking charts and tables to their data and can receive reports via the dashboard. Data can be imported using two basic structures: a flat file and a pivot table. In general, pivot tables create larger files with faster calculations while a flat file will create smaller files. Depending on the formula used to select data, it must be tested to analyze performance issues related to a business project.
Self-Service Big Data
In 2015, advanced organizations will progress toward data bindings on execution and steer away from a central structure to meet ongoing requirements. Self service data helps in two ways, It saves time and expenses for the IT department and provides direct data exploration for data scientists, data analysts and web developers. Historically big data required a huge investment in hardware and niche specific types of training for employees. Now with cloud computing and the "self service" mindset big data tools are competing for smaller and smaller business, thus lowering the barrier of entry. Self-service data also speeds the ability of organizations to tap into new data sources and respond to opportunities and security threats.
Enterprise architecture will be at the forefront as organizations quickly adopt it in their big data agendas. In 2015, the market will concentrate on the diversity across multiple platforms and the architecture required to integrate Hadoop into the organization's data center. When organizations utilize Hadoop technology, they are presented with a well-defined, detailed statement of requirements needed for big data applications, such as business continuity and high availability.
Big Data's Relationship With Cloud Computing
Big data involves the collection, storage, search, analysis and visualization of very large volumes of information. Typically managed as a pay-as-you-go service, cloud computing is a paradigm that lets individuals and organizations invest in digital resources that can be scaled or upgraded in real-time based on each client's needs and resources. The agility of cloud computing makes it a perfect match for big data projects since it makes the process of structuring and managing big data more cost-effective.
Making Sense of Big Data Projects
Although big data storage in 2015 has taken on a more sophisticated approach regarding how organizations conduct their operations, it may still need to be fine-tuned. Consumable data displayed in the form of a dashboard is an effective format to eliminate the need of opening many workbooks and worksheets to scan and find specific data. It also helps decrease the cost associated with big data maintenance and updates by assigning it structure. Moreover, cloud computing resources are needed to support big data storage as well, since it allows organizations to jumpstart and run their applications faster, and improves IT manageability and IT maintenance. As a result, IT can adjust its resources more rapidly to better handle unpredictable and fluctuating business demands. It makes sense, then, that organizations would use both of these resources to improve the efficiency of their operations and increase their profit margins.