![]() ![]() Just as the product cycles in application security became much shorter, so too did data processing cycles (from the time a team wants to collect and process data, or to analyze data, until they are able to do so).ĭata democratization means that more people are able to access more data, and that if security is not a constant part of the operation, the risk level for the organization with such high data exposure would be too high. It is commonly understood that security is not something that can be done in an ad hoc way, once per year or per quarter: data changes at a much faster pace, more consumers are constantly added, and data access keeps changing. In fact, DataSecOps should be viewed as the enabler of data democratization processes. It is an understanding that security should be a continuous part of the data operations processes and not something that is added as an afterthought. That means that the hotel chain now gets a lot of additional value, but their data operations need to enable such activities.ĭataSecOps is an evolution in the way organizations treat security as part of their data operations. Moreover, this can easily become a continuous project, to the point where perhaps guests making a reservation would get different information or rates based on some of these features. In most cases, this would make such a project a one-time effort to understand and learn from.įor a data-democratized organization, the data science team may get to work within a couple of days, or even on the same day, depending on the company’s data access maturity level. #SATORI SCOPE MANUAL#This data may be held in several different departments, and even across different geographies.īefore going through a data democratization process, it would take a lot of time and resources to answer such questions, and many parts of this project would require manual work. For that, they would need a lot of data to learn from. If a data scientist at a hotel chain wanted to know what features can predict a guest that would cancel their stay in the 24 hours prior to their arrival, they would be able to do a lot with this data (optimize processes, have better capacity planning, etc). This means that data ingestion, preparation, processing, and consumption are done in a more agile way, which requires the teams handling data-such as data engineering teams of different skill sets-to have more skills in scripting, automation, testing, integration, and production deployment. These processes, where more people and teams are adding and consuming new data continuously to the organization’s datastores, are causing data teams to evolve and adopt a DataOps mindset. In other words, with quite a basic skillset, as well as with the capabilities of powerful BI tools, vast amounts of people can make use of the organization’s data, in what’s called “Data Democratization.” The data consumers, in many cases (such as those in Redshift, BigQuery, Athena, and Snowflake), only have to write “select” queries to query data from huge tables, as if they were querying data from relatively small databases. ![]() There’s no wonder that organizations are moving or building large-scale data warehouses, data lakes, and data lake houses to the cloud. The elasticity and ability to store and process extremely large amounts of data without a prior investment in servers is one of the best catalysts for data-driven innovation in the last decade. And I’m not talking about RDBMSs, key-value storage, or other databases that moved to the cloud to support the applications that migrated to the cloud. Sure, it’s not 100% of the organizations or 100% of the data, but data is in the cloud. But cloud adoption, for data as well as applications, is a done deal. It took a while for data to follow applications and move to the cloud, at least en masse. #SATORI SCOPE SOFTWARE#The transition of applications to the cloud and the development of software in a more agile way brought DevOps, which-a few years and several data breaches later- sparked the realization that security needs to be embedded in the DevOps process, not an add-on to be plastered once everything is already in place. ![]()
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