While the growth in big data and cloud has multiplied our access to data, we are now faced with a new set of challenges in how many different applications and people need to be involved in the process to turn raw data into actionable insights. Take, for example, the multiple applications and handoffs required between capturing data, then preparing data, analyzing the data, visualizing data, and operationalizing data. The central issue with specialized solutions that only perform specific functions such as preparing data or analyzing data is the complexity of integration along with the time required to turn raw data into actionable insights.
The name of the game is speed coupled with quality insights. Let’s talk speed first. To get from A to Z, would it be faster to utilize one integrated platform or to take a best of breed approach? Every company I’ve talked with is struggling to get more done with fewer resources and every analytics project that sits on the shelf waiting in line with other competing priorities is putting an organization at a competitive disadvantage. While speed is crucial let’s not forget about a new challenge in the era of big data – quality.
Data quality is suspect when it comes to big data. Big data is gathering dust based on the fact that we don’t know if it’s of sufficient quality to use in the first place. The other issue is where to find it. I constantly hear that we need to include a data quality for big data checkpoint in the process to change the outcome of the analysis to yield high confidence in the analytical results. An integrated tool which adds data quality in parallel with data prep, while keeping track of data lineage, reduces risk and provides concrete proof that the data is reliable. What we don’t want are executives in the boardroom to be constantly questioning the viability of the analysis because no one can give a sufficient answer to the age old question, “Is the data being used for analysis accurate or garbage?”
Let’s continue to explore why less is more.
IT is on a mission to extract insights from the massive amounts of data an organization collects. With so much focus on data and the insights to be gained, IT has likely implemented a slew of manual point solutions to ensure every bit of insight is garnered from the data. When IT extracts that data from storage, they need to combine it with other data sets. This inadvertently opens up an opportunity for data to be changed. Additionally, it brings into question the quality of the data. The process is inefficient, drains resources and executives are not satisfied with the results. A company’s ability to quickly retrieve and confirm quality data can be a significant factor in determining the ultimate success or failure of a business.
It can take days, and in some cases weeks, if IT is forced to extract and process data. But what if you need timely data about customers to make informed business decisions that is time sensitive? By the time the data is extracted it can already be too late to obtain or retain a customer or put the right information into their hands. Generally speaking, if data isn’t orchestrated properly it leads to organizations reacting to problems after they have already occurred, rather than staying in front of customer concerns and proactively preventing them.
Additionally, IT teams must constantly keep an eye out for new data. When they do find new data they must load and prepare data for analytics. Manually managing this process is not only a lengthy process, but in the age of big data, obtaining real-time data analytics is expected and anything less means slower decisions, which can affect revenue. Executive teams are used to immediate gratification. Staying current to reflect business change means dashboards that represent real-time information. It also means managing data discrepancies in minutes, rather than waiting on IT who is already bogged down with various responsibilities.
IT teams want to satisfy the business user within their organization, but they simply do not have the time. In some organizations, the business user is constantly on top of the IT team asking them to extract and process data to make better business decisions, but with the IT team already overloaded, they can’t jump every time a business user needs them. How nice would it be to satisfy the business user by performing a data extract that the end-user can make changes to?
Let’s face it, separate solutions to handle each one of these functions is not effective which is also why adding a data quality step is seen as counterproductive to a process that is already too siloed and taking too long to complete. However, an end-to-end platform that can take the mountains of data available, and integrate, transform and handle these challenges can free up IT’s time with a user-friendly solution using real-time information. Talk about a win-win – IT now has time to manage other priorities and no one person is using multiple applications to pull down data facts necessary for the business.
A self-service, big data analytics platform designed to handle not one, but all the point solutions IT is using independently enables a business to automate, streamline their processes and add an important missing step – data quality for big data. Rather than multiple steps to handle data acquisition and preparation, data analysis and operationalization, or visualization, one platform can pull reports, detect new data and provide insights. In addition to access to specification logs, dashboard preparation, and visual workflow, the platform can empower the business user to aggregate and control data to accelerate and improve the subsequent data analysis process.
A point and click solution will not only save time, but increase productivity within budget.
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