Application developers, your day has come.
You are inhabiting a new world of development, where the business calls on you to create solutions that tend to be real-time and mobile. Digital transformation and developing unique applications are now core to driving the business forward. Essentially, application development has become the business.
The focus on delivering innovative solutions that often are customer- or partner-facing is a serious departure from the past when application development targeted tactical, internal business functions – before prepackaged on-prem systems and ultimately cloud-based SaaS tools took over that market.
A recent Couchbase digital innovation survey of 450 heads of digital transformation for enterprises across the United States, United Kingdom, France, and Germany revealed that 80 percent of companies are at risk of being left behind by digital transformation while 54 percent believe organizations that don’t keep up with digital transformation will go out of business or be absorbed by a competitor within four years.
Today, the stakes are high for application developers. And, in an app-driven world, more and more of you are turning to the NoSQL database. It’s more agile, responsive and scalable.
So, achieving market-differentiating apps to create a better customer experience requires adopting a new organizational paradigm: leveraging non-relational databases (NoSQL); building another level of development expertise; and driving optimal data performance. And when the stakes are this high, there must be an investment in data governance, backed by data modeling, to mitigate risk and provide accurate, empowering real-time analytics and business intelligence.
It’s agile, fast and flexible … and doggone it, people like it
NoSQL databases were designed with modern IT in mind. Their more flexible approach enables increased agility for development teams. It shouldn’t come as a surprise that the NoSQL market is projected to be worth $4.2 billion by 2020.
Generally, NoSQL databases are better equipped to deal with other non-relational data too. As well as JSON, NoSQL supports log messages, XML and unstructured documents, avoiding the lethargic “schema-on-write,” opting to “schema-on-read” instead.
Data models also can be evolved on the fly to account for changing application requirements, enabling businesses to adopt an agile system of releasing new iterations and code. They’re scalable and object-oriented and also can handle large volumes of structured, semi-structured and unstructured data.
Rapid delivery is the rule, with products released in usable increments in sprints as part of ongoing, iterative development. You can move from conceptual models for defining high-level requirements to creating low-level physical data models to be incorporated directly into the application logic. This route facilitates dynamic change support to drive speedy baselining, fast-track sprint development cycles, and quick application scaling. Logical modeling then follows.
However, the business can’t favor agility, speed and flexibility over the need to stringently manage its key data assets. Differentiated and high-performance applications backed by NoSQL databases clearly are poised to bring tremendous benefits to organizations, but integration of the data contributing to and resulting from these real-time apps is critical to the business being able to use those assets effectively.
Without a single, standardized end-to-end view of the customer profile as it evolves, for example, it will be challenging to move ahead with activities such as targeted, real-time cross-selling and up-selling apps that track with an individual consumer’s changing needs.
Therefore, if the business is to successfully use data sets from diverse sources in the service of real-time application analytics, as well as business intelligence reporting, there can be no holes in data governance.
Data governance + NoSQL
Coming off the heels of the Facebook fiasco, and with major regulations like the General Data Protection Regulation (GDPR) taking effect, the importance of data governance cannot be overstated.
However, it can be challenging to use the latest NoSQL database technologies while also attempting to maintain the integrity, quality and governance of underlying data. Logical data models, where an entity relationship diagram (ERD) provides a picture of different data categories and how they relate to one another, offer a strong underpinning for data governance.
Database administrators, data architects and data stewards know the importance that logical ERD has for cross-domain modeling and management. But logical modeling has not been widely embraced by NoSQL developers, who favor physical models, so it’s hard to seamlessly integrate NoSQL data in its native form into the larger world of data.
This needs to change – you need to garner support for logical modeling to reference many data sources and ultimately map back to a single set of enterprise data elements with appropriate governance pieces for security, privacy and other requirements. With an appropriate data governance tool, it will be possible to link logical to physical modeling and representations of NoSQL databases.
For instance, a company may use a tool to force NoSQL developers to select from an existing data glossary of objects drawn from a logical ERD before they create a new collection or physical object, and in that way, achieve more holistic governance. Other aspects of governance to consider are the benefits that come from providing collaboration on data models across business and technology roles. That way, individuals can bring different perspectives to the table, potentially reducing risks in a model that those serving other functions might not see and helping mitigate them as part of the process.
Successful data governance that supports critical integration efforts for production applications will be empowered by capabilities such as cohesive business glossaries, data dictionaries, data catalogs and consistent data exchange across the people, processes and systems that manage and protect data.
Fortunately, as NoSQL has grown in popularity, quality tools have emerged to model, govern and manage your NoSQL data as effectively as for relational databases.