Technology’s role in the evolution of useful data.
GUEST COLUMN | by Brian Ludemann
It is well known among education technologists that adoption of technology in our industry has always lagged behind others. There are many reasons for this – the outdated infrastructure, the expense of providing devices for all students, funding, limited direction from district leadership. And for many of the early adopters, many of the applications were half-baked. Applications were small in scope, bandwidth was miniscule, and solutions were mostly just paper substitutes. In regards to data, aggregation was at best, only happening at the building level.
Systems, and the companies that develop them, need to have education in their DNA to be relatable to an educator’s data-to-day life.
This slow adoption has had its benefits, however. It has allowed our industry to avoid the struggles that the early adopters had to withstand and benefit from those lessons learned. Today, we are adopting technology at a point further along the industry’s maturation, and in many cases, without the hassle of having to support as many legacy systems as the technology moves forward. Now that SaaS-based solutions have become the norm, and large amounts of data are being stored and manipulated in the cloud, a whole new realization of how we can use data to help administrators, educators, and students is coming to the forefront for the first time.
Data Analytics Lead to Being Data Driven
When vast amounts of data end up in a centralized location, performing data analytics becomes the most logical next step. But educators need technologists to take it a step further. Providing the raw results of analytical processing might not be enough for the time-strapped teacher. Results not only need to be directly correlated to the information a teacher deals with day to day, it also needs to be actionable. Many data warehouse and analytics consumers are decoupled from source systems of the data. In the case of teachers, using the results in an actionable way, in real time, is truly being data-driven.
Being data driven starts with obtaining quality detailed (source) data. Deficiencies in data become compounded when analysis is performed on top of it. Further, decision makers need to acquire systems that allow easy integration, movement, and correlation of data. Ultimately, data will need to converge to a centralized data warehouse, where the data analysis can be performed, in some cases across heterogeneous platforms. There is an awareness of this need forming in the market however. Standards compliance and ubiquitous semantics are becoming more widespread across the industry. Decision makers should consider the underlying architecture when making a buying decision. The explosion of the EdTech market is having an effect on this. The days of a single monolithic system for all the technology needs of a district are ending and solutions and strategies that aid internal IT to connect disparate systems are becoming more common.
Analytical results can provide insight into data that would otherwise go undiscovered. But unless some action can be taken, that value is lost. Just as the data flowed into a warehouse, the results of the analytical processing must somehow flow back into the source systems or workflows so that meaningful action can be taken. This may seem obvious, but most applications are narrowly focused on the transactional tasks they are designed to perform, leaving the responsibility on the educator to take meaningful action. Data driven educators are ones who take that action and develop an awareness of the effects. Unfortunately, manual consideration requires an awareness of many aspects of the data – how it was derived, what systems the data was sourced from, and what aspects of their curriculum should be adjusted, can take time and a broad scope of insight. Ideally, the systems themselves will provide mechanisms to bring data back into the system. In either case, a feedback loop (See diagram) is established and we can actually measure the effects of a data driven environment.
Barriers to Being Data Driven
When it comes to collecting and accessing student data, privacy and security of the data are primary concerns. The acceleration of technology adoption in our industry is coming at a time where the public in general is oftentimes on the receiving end of data breaches, spam, and overzealous sharing of data by the software vendors they use. Trust is eroding, most feel that they have little control over who can access their data and how this information is being shared. Our industry has compliance guidelines to adhere to, but with a lack of trust, compliance standards can have a diluted impact. However, while educators should be careful with their data, these fears should not stop them from being data driven. Any time there is data in a system, there is a chance that someone will use that data for malicious purposes. However, in the grand scheme of things, data breaches are rare. The bottom line is that the reward of being data driven is so much greater than the risk.
Technology Helps Make the Case for being Data Driven
It’s not enough for software to simply collect data any longer. Systems, and the companies that develop them, need to have education in their DNA to be relatable to an educator’s data-to-day life. The needs of the educators are unique when compared to other industries. The time crunch felt by educators cannot be underestimated. This, along with a very wide gap in technology proficiency, will present significant challenges for districts that aim to be truly data driven. As the industry presents many new options to IT leaders, educators, and administrators, decision makers need to keep all these factors in mind for each system they implement if they aim to be truly data driven.
Brian Ludemann is Director of Application Architecture and Development at Apperson.