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Finding great data could be the most challenging facet of Big Data Analytics
Many companies that are beginning to work with Big Data Analytics are looking at solutions that theoretically enable them to be aware of every fluctuation in incoming data. For example, if there is a spike in sales, workers want to know what caused it and how to react. But in addition to just being alerted that changes are occurring, users should also be able to manipulate the data to find root cause. These practical needs, more than an abstract consideration of the numbers, drive analytics demand and desire. However, the problem with this approach is the data itself. Big Data Analytics calls for a more flexible, complete and holistic data source that can be used by the business people who hold the subject matter expertise without requiring lengthy integration projects that rely heavily on expensive services. So how can Contextual Big Data help solve the problem?
Spreadsheets still standard
The new era of data analytics is on the way. However, the technology still prevalent in the industry is doing its best to hold things back. Spreadsheets are the industry standard for a large percentage of data analysis. They have become ingrained in the workplace because of its familiarity and simplicity of design. Many workers who interact with data every day have no programming experience, meaning their criteria for application selection hinge on ease of use rather than depth of insight. This tool, however, tends to fall short of the high-level analytical needs of modern businesses. Big data sources, for example, are unusable in spreadsheet programs, with scale and format that defy conventional usage.
Big data tools often deal with data that is in motion, constantly incoming and frequently updated. Spreadsheet-friendly data sets are static, taken from past events. Companies that want instant feedback on ongoing events will be ill served by any spreadsheet application.
Visual drawbacks
Sometimes, even specialized tools struggle with the intense data volumes currently facing companies. Products that center only on visualizing information sometimes fall short when the resources used are extremely large. Creating tables and graphics, even when successful, only solves part of the problem.
Tools like Tableau exist in the visual space. Companies can use them to take stock of the market situation, but such analyses are predicated on a manageable size of data, in a format that can easily be plotted on a chart. Such programs are closer to a modern solution as they can handle real-time data streams, but they are still not completely up to the task of high-powered information management. In addition to visualizing data, users need to manipulate the data, and these tools struggle with that requirement.
The problem
The problem with big data analytics, much of the time, is the data itself. Companies are simply buried under avalanches of fast new information, armed with tools better suited to small, standardized information sets but going up against the most copious and varied bodies of data ever presented. Dividing data by purpose on acquisition presents new troubles, as companies are then unable to re-integrate the archives.
Companies hoping to solve information scale problems sometimes turn to Splunk. This tool, while it may offer a solution to companies dealing with high volumes of real-time data, also brings its own set of challenges. For example, effective setup and deployment of such systems require a deep technical knowledge of the data in question with regard to its log format. Inexperienced coders working with such systems could find themselves unable to extract meaningful results.
Data in context
Contextual big data technology could represent the answer to companies’ pressing big data questions. Contextual functions work automatically based on rules. Once developers tell the program what type of data would be helpful, it acts without constant input, making information sets usable and helping gather insight without causing trouble later or confusion in the present.
Worrying about the content and quality of data sets has become an outdated notion. With OpTier systems automatically selecting powerful data sources for companies to use, employees from IT to the business sections can stop fretting about data collection processes and begin enjoying effective new analytics approaches dictated directly by their needs.
When they work with OpTier, companies gain access to development tools that are powerful enough to draw high-level insights from data sources that are both high-speed and large volume. These programs are so easy to create that business users with negligible tech training can design their own to suit their needs. OpTier offers contextual big data tools that can help companies solve their problems in four distinct ways, rising above the insights possible with outdated spreadsheet or visualization dependent systems.
-Our tools go directly to the source, capturing data as it is collected in real time. Sources are clearly delineated as historic or real-time. And the systems automatically optimize storage performance for this complex combination of inputs.
-With our technology, workers can interact with the information even as it streams in. No matter what the speed or source of the information resource in question, IT users can create a small-scale operation and apply it to huge data sets with patent-pending processes. Whether workers need a single answer to a vital business question or a constantly updating source of insight, OpTier can provide the appropriate tools.
-Employees get the insights they are looking for. The demands of the business dictate the performance of OpTier’s contextual big data technology, with storage handled automatically. With that complex process safely taken care of, business users can spend more time asking questions and getting answers and less time worrying about the processes holding it all together. Rather than being controlled by the dictates of data, the user is always in control.
-Users of our systems can view the information in intuitive and exciting ways. Whether a web report, a dashboard with interactive features or a tool that enables deeper exploration of the data store, workers can choose a method that suits their individual needs. Powerful new HTML5 web tools mean companies can always have an effective and adaptive framework to present results. Our consistent data structure and integrated data processing and visualization pipeline suddenly makes it possible to close the loop once again. The business user can see and understanding the data, manipulate it directly, and iteratively visualize the results in charts and graphs.
So as the goal moves from alerts and reports to flexible analysis, more companies will come to realize that the data is the biggest limitation facing them in big data projects. Contextual data can help. For more information about OpTier’s contextual big data analytics programs and how they can help your business, Contact Us.