Data in the Cloud – Empowering Everyone to be a Data Analyst

SADA Says | Cloud Computing Blog

By SADA Says | Cloud Computing Blog

Data Analyst

With the innovation that comes with the cloud, your employees no longer need to be a “data analyst” to use data in a powerful way to do their jobs. In 2017, data-forward executives are waging both a cultural and structural battle for the best use of data within their organizations. According to Gartner in a late 2015 report, “while 80% of CEOs claim to have operationalized the notion of data as an asset, only 10% say that their company actually treats it this way.” In just the last two years, however, cloud ecosystems have made gigantic strides towards easier data-discovery and self-service for business staff end users. Google Cloud Platform alone has seen sweeping changes to its user-friendly BigQuery and Data Studio services for rapid BI development. And as more solutions follow suit, the structural barriers to data democracy are beginning to fall one by one.

For skeptical data execs on whether their employees can all truly play the role of a data analyst, an organization’s sprawling infrastructure is the first hurdle to overcome. Common barriers to fostering a data-driven culture still include data silos that lead to lack of communication around the business value of data, simply because staff struggles to understand where and what the data is. The structural and cultural shift needs to happen simultaneously with the former fomenting the latter. And although structural efforts to democratize data can be cumbersome, data-driven companies have repeatedly been reported to outperform their competition.

Step 1: Ensure data is accessible to those who need it (it’s not as hard as it used to be)

Access is the first problem. Modern data managers deal with sprawl problems of every flavor – transactional, streaming, legacy, local, raw – the idea of getting all that data into one place, cataloging it, and training business staff to understand it like a data analyst sounds like a daunting process. It requires collaboration of the data scientists, engineers, and analysts, all using their own platforms of choice on their local machines. By the time a manager has finally set up a nice data warehouse for analysts, there is often a time-to-insight problem: the data is old or the acute business need for it no longer exists.

In response to these concerns, Google Cloud Platform focuses its solutions on organizations struggling with data sprawl, access, and management issues. Their Cloud Dataproc service provides templates for heavy lifting on both batch and streaming data, easing the life of your data engineer. Simple JSON files can be attached to databases in Cloud Datastore to quickly catalog information and to aid in discovery through easy querying in the BigQuery UI. This allows an organization to spend less time tracking down data owners, importing, or manipulating and more time with analysis.

By housing data processes centrally, there’s also more visibility for technologists to see the downstream effects of their efforts, all the way to marketing dashboards and BI tools created in Data Studio. Data engineers and scientists can see who is using data in their domain and how they are using it and help proactively facilitate efficient use without a need for meetings and emails.

Step 2: Make interaction with data intuitive and user-friendly for your new data analysts

While all cloud solutions seem to offer jaw-dropping processing speeds, fluid compute power, and competitive, fluid pricing, Google Cloud Platform stands alone in its usability. It caters to both deep and casual users by blending its consumer-facing aesthetics with its enterprise products. Analysts that tend to favor Excel, SQL and traditional OLAP tools can use BigQuery to construct simple SQL statements in seconds. Even less tech-savvy users can run and configure parameterized reports in Data Studio, which boasts the same intuitive user interface as all tools within Google’s consumer ecosystem like Gmail and Sheets.

Step 3: Tier accessibility and focus on data governance

When envisioning a world of “data for all,” it’s natural to picture a worse cast of data anarchy and security nightmares. When infrastructure is properly managed, these scenarios are far from reality in GCP. For Cloud Datastore users, although there is no user-facing UI, CRUD applications can be built on top of Datastore to allow internal, non-technical staff access in a controlled, targeted way. Execs can also rest easy knowing that GCP’s IAM can control who has access to data in BiqQuery and what permissions they are allowed, that solutions can be HIPAA compliant, and overall transparency in the cloud is much preferred to employees sharing data across multiple platforms. And gone are the days of waiting for IT to provision a new cluster and compute power: Google does that for you and charges only for what you use. This setup catalyzes rapid data-driven decision making from empowered end users.

The role of modern CDOs is to lead their organization’s structural and cultural shift in the way it treats data as an asset and allows everyone to be a data analyst. While structural overhaul is not an overnight process, Google’s ecosystem offers a host of services that caters to almost every problem of data sprawl and misuse.

For more information on Google Platform Cloud, visit SADA systems, download our brief, “5 Ways the Cloud Enables Businesses to Innovate,” today!



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