Data-driven businesses that succeed strive to improve data quality and reduce data organization time. It is important to have data that can be analyzed quickly and efficiently. The best way to achieve that goal is to make data smart.
We’ve seen that time is already a problem for organizations driven by data because of the delay in gathering and preparing the data for traditional analysis. IDG Research’s 2016 Data and Analytics survey found that 90 percent of respondents experienced pain in areas like data access, data transformation and data collection.
Data volumes, already large and growing, are further complicating matters. This is accelerating the pace at which data flows into organizations. Companies rely on the data to optimize user experience. However, very few companies can pull high quality data from the flood.
To extract the full value from organizational data, it is necessary to quickly identify which bits are most important, ensure data quality, and then add context to make data actionable. Smart data is created when you are able to do this.
Smart data is different from traditional data collection and analysis methods. This has profound implications on everything, from customer experience and operational efficiency to security threats and improving customer service.
How Smart Data Helps
Although smart data definitions can vary, they are generally defined as data that has been prepared and organized at the point of collection to ensure that it is available for analysis at the highest quality and fastest possible.
According to FedTech, Donna Roy, the executive director of U.S. Department of Homeland Security’s Information Sharing and Services Office said that her teams spend approximately 80% of their time searching, ingesting and getting data ready for analysis. Roy believes that smart data will allow agencies to work faster and more efficiently by eliminating the need to ingest large amounts of data.
FedTech paraphrased Roy’s definition of smart data as “data which is independent from software, apps, devices, or networks, but still can be actionable.” It is also self-describing and self protecting. It is defined by its context and semantics.” The context of the data is attached closer to the source.
Smart data is information that makes sense, Wired reports in the article “Big data, fast and smart data”. This is what Wired calls the difference between seeing a long list describing weekly sales and being able to identify the peak and trough in sales volume over time. Algorithms transform meaningless numbers into actionable insights. Smart data is data that intelligent algorithms have extracted patterns and signals from.
Traditional analytics uses data to gather, process, and then groom it on a set schedule. This could be daily or weekly. This workflow can lead to results that are outdated by the time they are considered. Smart data is accessible and processed immediately after collection, which reduces the time required to prepare the data.
What does this all mean for the business world? Smart data is a way for companies to extract relevant data from the slew of data that they are receiving. It is hugely beneficial to know what your data says earlier in the digital age of business. Smart data can be a crucial part of a variety of activities, including healthcare monitoring and patient care, big data analytics, cloud migration, network performance management, and cloud migration.
Consider, for instance, the problem that Bill Gillis, CIO at Beth Israel Deaconess Care Organization, Boston, cited in part one. His company wanted more information about patient health through claims data. However, that data is usually not available until 90 days after the event that brought the patient to the healthcare provider. This is too long to respond in a meaningful manner, rendering the data fairy meaningless. The data would be available much sooner if it were made immediately.
These are the key considerations to help you build a smart data strategy in your company.
- Take into account the data source. It is important to identify the best data sources. Some network monitoring tools use unstructured machine information (log files, SPNM, etc.). This data is indexed and archived to allow for later analysis. This approach has many limitations. It produces a lot of data that must be sorted, but it also collects only data that can be log, which leaves you with potential blind spots. The second is that the process can produce old data. The best smart data strategy is to use wire data for network visibility. It provides a complete view and can be accessed, collected and transformed in real time.
- Ensure data quality. By some estimates. Companies lose 12% on average for bad data. The old saying “garbage in, garbage out” has a much more serious meaning due to the many critical uses smart data is being used for. This includes business analytics, data security, and operations roles in application performance management. A consistent and cohesive approach is required to build data quality throughout an organization. This is something that is outlined in the corporate data governance handbook.
- Examine the need to make organizational changes. Smart data has a faster rate of return than traditional data analysis. This means that there are more opportunities for people to act on data closer to their point of collection. This will impact both technology strategies and organizational structure. Are you able to capitalise on data more quickly? How your teams are set up to handle the data. Do you need a more distributed structure? ).
- Embrace automation. The need for tools that automate data collection and data transformation is vital. This will increase as you attempt to extract value out of the ever-increasing data volumes from an increasing number of sources (read the Internet of Things). It is impossible to find a better way to extract value from the ever-growing data volumes coming from an ever-increasing number of sources (read: Internet of Things).
There are many ways to approach this problem, as we have already mentioned. Smart data applications are used to manage network and application performance. This requires instrumenting the entire network to ensure visibility. It can be too expensive and time-consuming to transfer hardware-based methods to cloud-based environments. This can prove to be costly for many companies, but smart data is best collected directly at the source. It’s important to look for an alternative. Look for software tools that can reduce costs and extend reach to mixed environments like cloud or virtualized environments.
Although it may be difficult, digital transformation will succeed if you have good data and can act quickly on it. Smart data will help you make better decisions quicker, and the companies who do it best will be the ones to win.