A famous Stanford professor said a few years ago that “IT doesn't matter” which caused a big uproar across the enterprise IT organizations worldwide. What he really meant was, IT was not a competitive differentiator anymore and it is taken for granted. This is where deploying tonnes and tonnes of internal and external data that enterprises collect and have access to come into play. Analytics is the function using which companies can process and create intelligence which can be deployed to provide the competitive differentiation. In fact, in today's modern enterprise there is no choice but to have world class analytics practice to support business to continue to be competitive in the market.

Blumetra brings decades of expertise in data & analytics practices with primary focus on helping companies gradually build infrastructure and assets to support the growth and transformation. Our analytics framework for companies enables companies to fully leverage the capabilities that are on the offer:

Enterprise Data Management

Data is the new oil, one of the major challenges modern enterprises face today is the struggle to collect, orchestrate, and manage internal that is moving thru their internal systems and external syndicated data. Without a holistic enterprise data management strategy, businesses are unable to realize its full value.

Business leaders can gain a key competitive advantage from their data ecosystems, but research suggests that most leaders realize they don't have their data ducks in a row. Poor data quality affects everything from analytics, business intelligence and decision making (wrong conclusions drawn) to employee productivity (lost time due to rework and poor communication).

Enterprise data management (EDM) refers to a set of processes, practices, and activities focused on data accuracy, quality, security, availability, and good governance.

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Data Science

Data science combines domain expertise, programming skills, and knowledge of mathematics and statistics to extract meaningful insights from data. Data science practitioners apply machine learning algorithms to numbers, text, images, video, audio, and more to produce artificial intelligence (AI) systems to perform tasks that ordinarily require human intelligence. In turn, these systems generate insights, predictions and recommendations which analysts and business users can translate into tangible business value.

• Artificial Intelligence: computer systems that perform tasks that normally require humans
• Machine Learning: systems that automatically learn and improve from experience without being explicitly instructed
• Data Mining: process of discovering interesting and useful patterns and relationships in large volumes of data

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Data Products

Data Products that are fuelled by data and machine learning and can be used as a powerful tool to aid in decision making.

The lifecycle of a so-called “data product” mirrors standard product development: identifying the opportunity to solve a core user need, building an initial version, and then evaluating its impact and iterating. But the data component adds an extra layer of complexity. To tackle the challenge, companies should emphasize cross-functional collaboration, evaluate and prioritize data product opportunities with an eye to the long-term, and start simple.

Stage 1: Identify the opportunity, Data products are a team sport

Identifying the best data-product opportunities demands marrying the product-and-business perspective with the tech-and-data perspective. Product managers, user researchers, and business leaders traditionally have the strong intuition and domain expertise to identify key unsolved user and business needs. Meanwhile, data scientists and engineers have a keen eye for identifying feasible data-powered solutions and a strong intuition on what can be scaled and how.

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Stage 2: Build the product, De-risk by staging execution

Data products generally require validation both of whether the algorithm works, and of whether users like it. As a result, builders of data products face an inherent tension between how much to invest in the R&D upfront and how quickly to get the application out to validate that it solves a core need.

Teams that over-invest in technical validation before validating product-market fit risk wasted R&D efforts pointed at the wrong problem or solution. Conversely, teams that over-invest in validating user demand without sufficient R&D can end up presenting users with an underpowered prototype, and so risk a false negative. Teams on this end of the spectrum may release an MVP powered by a weak model; if users don't respond well, it may be that with stronger R&D powering the application the result would have been different.

While there's no silver bullet for simultaneously validating the tech and the product-market fit, staged execution can help. Starting simple will accelerate both testing and the collection of valuable data. In building out our Skills Graph, for example, we initially launched skills-based search — an application that required only a small subset of the graph, and that generated a wealth of additional training data

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Stage 3: Evaluate and iterate, consider future potential when evaluating data product performance.

Evaluating results after a launch to make a go or no-go decision for a data product is not as straightforward as for a simple UI tweak. That's because the data product may improve substantially as you collect more data, and because foundational data products may enable much more functionality over time. Before canning a data product that does not look like an obvious win, ask your data scientists to quantify answers to a few important questions. For example, at what rate is the product improving organically from data collection? How much low-hanging fruit is there for algorithmic improvements? What kinds of applications will this unlock in the future? Depending on the answers to these questions, a product with uninspiring metrics today might deserve to be preserved.

Analytics Delivery

The intelligence and signals that are gathered in the enterprise warehouse and the data products can be delivered to the end users in either a push model or a pull model. In a push model, based on some exceptions which are configurable, user will receive signals using a standard mechanism to take action. In a pull model, user will go to a central place (either a porta or a platform) to access the data assets that have been developed for his or her use and take action.

The table on the right highlights typical methods used and some description on how each of those works. It is not uncommon to have more than one way using which intelligence is delivered.

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Data Observability, Research & Insights

As the amount of data, the pipelines and its importance in the enterprise grows exponentially, traditional mechanisms to monitor the timeliness, accuracy and completeness of data are proving to be inadequate. In today's world where data products are considered as monetization tools, this is un-acceptable.

“Data observability” is the blanket term for understanding the health and the state of data in your system. Essentially, data observability covers an umbrella of activities and technologies that, when combined, allow you to identify, troubleshoot, and resolve data issues in near real-time.

By encompassing a basket of activities, observability is much more useful for engineers. Unlike the data quality frameworks and tools that came out along with the concept of the data warehouse, it doesn't stop at describing the problem. It provides enough context to enable the engineer to resolve the problem and start conversations to prevent that type of error from occurring again. The way to achieve this is to pull best practices from DevOps and apply them to Data Operations.

All of that to say, data observability is the natural evolution of the data quality movement, and it's making DataOps as a practice possible. And to best define what data observability means, you where DataOps stands today and where it's going.

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