What are you trying to solve with your data?
Like any journey, defining the destination is critical. There is no point in embarking on a data modernization initiative until you determine your end state - what are you trying to accomplish with your data? For some, simply capturing their data more efficiently is enough, where others strive to use their data to glean insights to fuel their business decisions.
Our data architects listen first, helping you assess and determine your short- and long-term data goals. Then, once the end goal is defined, they bring their expertise to the platform and tools discussion, ensuring you make best-fit decisions for your business.
Start by understanding data type, location and format.
Data can come from many sources — including SaaS data, in-house applications, databases, spreadsheets or even information scraped from the internet. Because of the myriad of sources, successful data projects must include categorizing the data to determine the data type, location and format.
OneNeck data experts work with you to understand and document your current data state, so that right-fit decisions can be made that mixes and matches components to create a high-performance, agile and scalable data pipeline that aligns to the business.
The Microsoft Azure platform is particularly dynamic for data modernization because it can be customized to any organization’s business requirements. Structured or unstructured data; storage, compute or visualization; AI, ML, ETL, ELT — with Azure, the configuration options are virtually limitless.
DataOps success requires a modern foundation.
Traditional data platforms are constrained by physical infrastructure limitations, siloed operations and the inability to scale, and thus lack the speed, flexibility and scalability required to serve insights at the speed of the business.
To meet the demands of the business, enterprises can now take a platform approach, using Platform-as-a-service (PaaS) offerings like Microsoft Azure reduce the complexity of the data pipeline and provide a standardized set of tools and services for the business to leverage — without having to build them from scratch.
With Azure, organizations no longer face the limits of physical disk and server processing power in their data centers. Instead, they can get out of the relational database management system (RDBMS) box and store structured data in NoSQL storage via Cosmos DB.
In addition, they can store unstructured files in the petabyte-scaled Azure Data Lake and ingest data with a variety of services including IoT Hub, Event Hub and Data Factory. Using Azure services, the data is encrypted in motion and at rest, and once the data is available in Azure, organizations can gain deep insights into it using AI and analytics services like Stream Analytics, Cognitive Services, Machine Learning and Power BI.
With Azure, enterprises can significantly increase their business agility and return on their IT investments by eliminating redundancies, consolidating their IT footprints, and using (and paying for) only what they need. At the same time, the platform’s robust security and governance capabilities and tight native integration among data tools helps to ensure that data remains safe, while still enabling the free flow of information in an ecosystem built for AI and intelligent apps that gives companies a natural gateway to the cognitive enterprise.
Get real-time insights that drive business decisions.
Business intelligence and analytics, in a form that executives can understand and analyze, is crucial to accurately forecast the future state of the business and anticipate customer needs. But organizations must do more than just hire smart people and give them access to data; you also need an analytics platform that serves data scientists, business intelligence users and executives alike.
With Microsoft Azure, OneNeck data experts help you turn data into insights:
“We knew our aging, on-premises infrastructure would be expensive to upgrade and maintain. It was time to explore solutions that would allow us to avoid the cost of paying upfront and shift to an operating expenditure model and pay only for what we need, when we need it.”
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