We encounter analysis algorithms every day based on how we behave in e-commerce or social media channels. Advanced analytics can also be the basis for new, personalized products and services. For example, at-home DNA-testing kits have turned analysis of DNA data into personalized, life-enriching experiences, such as learning new details about your lineage and why you are sensitive to certain foods. Consumers are attracted by a personalized experience — and it’s no different in the B2B space.
Advanced analysis of operational data is uncovering opportunities to tweak assets (buildings, equipment, systems, fleets, investments, loyalty programs, etc.) to reach heightened levels of efficiency and reduce costs. By comparison, advanced analysis of B2B information can enable fine-tuning processes, systems and decisions far beyond what enterprises can accomplish without it – in tasks like choosing vendors, planning resources and making performance improvements. That’s why having data engineering experts on hand is crucial to leveraging operational data to drive B2B success.
What do we mean by big data strategy? Simply put, it’s designing a set of systems and processes that analyze and systematically extract value-creating insights from large data sets. Big data challenges can include capturing, storing, analyzing, sharing, visualizing and archiving large amounts of relevant data. Partnering with a firm with the right technical expertise and access to appropriate tools can be a faster (and more successful) path to leveraging big data analytics for business value, compared to doing it in house. Most organizations don’t have the latest tools and expertise in this area in house, so attempts to build these capabilities often don’t achieve optimal results.
Getting your big data strategy and infrastructure right is important because any organization with a sizable amount of unanalyzed data is likely sitting on a lot of hidden opportunities. For example, asset-intensive industries such as manufacturing are analyzing operational data to be more proactive about machine maintenance. Software can analyze mountains of historical operations data and vendor-provided machine data to predict when machine components will start to fail. When manufacturers can schedule a replacement rather than shut down a working line whenever the part fails, it saves the company significant costs of lost labor and production.
Retail e-commerce portals, websites and social media applications feed product suggestions to buyers based on digital behavior. This is another example of big data analytics at work. Product offerings are matched to consumer profiles/behaviors and timed to appear during peak interest periods. Take the online fashion retailer, Revolve, for example. They take user engagement data from Instagram and turn it into real-time trend analysis using their platform’s algorithm. This allows them to stock up on clothing items consumers actually want, keeping inventory costs low. By “listening” to their user data, Revolve has seen an increased profit from $5.3 million net income in 2017 to $31 million net income in 2018.
In fact, the value potential of data will keep rising, alongside the amount of data, which is why we’re focusing on our Big Data practice at Nortal. A key part of that practice is our partnership with Databricks, which provides a unified analytics platform powered by the open-source Apache Spark analytics engine. Internet powerhouses such as Netflix and eBay have deployed Spark at massive scale, and it has become one of the largest open-source big data communities.
If demand for data scientists is any indication, organizations recognize they can extract a lot more value from their data. These specialists are highly valued for their skill at recognizing patterns in data and building predictive models based on pattern analysis. But data scientists can’t do their jobs well without quality data engineering — something that is often overlooked or confused with data science.
Data engineering includes:
Companies might assume a data scientist has data engineering skills. This often not the case. Likewise, relying on database administrators (DBAs) can be tricky because they have other core responsibilities and might not be able to dedicate sufficient time to the requirements of data engineering. Traditional application engineers usually don’t have the right skills to engineer data for data science.
Without solid data engineering, data scientists can’t build models that are consistently reliable, both in performance and insight quality. This is why it’s beneficial to partner with a company like Nortal that has skills specific to data engineering and access to partners like Databricks.
Big data analysis is creating opportunities to personalize customer experiences everywhere, drive greater customer satisfaction and increase the bottom line. Enterprises that want to derive value from their data should consider engaging high-quality data engineers early on, to lay the foundation that enables data scientists – and their organizations – to be successful. Contact us to learn more about Nortal’s Big Data practice.