5 Mistakes to Avoid When Building an Enterprise Data Warehouse - InformationWeek
IoT
IoT
Data Management // Big Data Analytics
Commentary
5/17/2018
07:00 AM
Bobby Beckmann, CTO, Lifesize
Bobby Beckmann, CTO, Lifesize
Commentary
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5 Mistakes to Avoid When Building an Enterprise Data Warehouse

While it's a challenging and time-consuming process, the creation of an enterprise data warehouse stocked with the right data will ease management, empower the business, and enhance the customer experience.

As business processes evolve and become even more digital, they are generating a staggering amount of data from within the organization, as well as through customer interactions. As a direct result, IDC estimates the amount of data in the world will reach 163 trillion gigabytes by 2025. For perspective, that equates to watching the entire Netflix catalog 489 million times!

I’m not saying that data isn’t good, because it is, actually. It’s very good! But considering the volume, it’s important for organizations to have a Business Intelligence (BI) strategy in place — including an enterprise data warehouse (EDW) to organize, secure and leverage data — because there are serious business implications and missed opportunities if it isn’t captured correctly.

The benefits of an EDW for businesses of all kinds is tremendous, from using data to maximize operational efficiency to predicting potential customer issues before they happen. To ensure that you can use your data for maximum BI and analytical success, watch out for the following data capture mistakes that can cause long-term issues:

Not collecting enough data. It’s true, there’s more data than most businesses know how to handle. Perhaps what I’m saying sounds counterintuitive, but for businesses to take advantage of all that BI has to offer, the ability to see the full picture is always better than just a small snapshot. From what I’ve experienced, data is typically not optimized because the amount stored in an EDW can be overwhelming. There is always light at the end of the tunnel, however, in capturing as much data as possible in the first instance and then slicing and dicing as needed. For example, a SaaS company should collect all product logs since it’s competitively advantageous to know exactly who is using what product. Not only that, but it allows for an accurate measurement of customer experiences, which is more valuable than gold for a growing business. Having historical data available also helps to map trends and gain insight into product issues, adoption rates, etc., as a business grows.

Not developing SMART KPI’s. So, you’ve captured all the data. Now what? It is imperative to filter the relevant and actionable data to develop SMART (Specific Measurable Attainable Relevant Timely) KPIs (Key Performance Indicators). As mentioned, it is very possible to experience data deluge and lose valuable time and money trying to measure everything. For IT, being driven by business analysis, creating SMART KPIs upfront along with department heads, and developing appropriate filters gives you an opportunity to become an even more strategic partner.

Too many times this step is skipped. Without buy-in from all departments, the implementation can overshoot the objective, budget and timeline. At Lifesize, we deal with millions of data points every day while running our service. Working across groups, we determined what data was instrumental to understanding what the customer experienced while making a call using Lifesize. By using the right set of tools, we were able to go deeper than the normal “Call Drop” measurement and start to understand different metrics that impacted the quality of a call. By having this data, we were able to prioritize engineering tasks to improve the quality of the service. Without filters, you end up with a complex system that is difficult for all parties to use and often out of touch with the needs of the business. The benefits of a well-designed EDW to aid in a BI program are nearly limitless, allowing for greater understanding and more informed business decisions.

Not putting the data to work. Capturing the data is just the beginning of the journey. It is what you do with the most important data that truly matters. While that may sound easy, it isn’t. Businesses should jump right in and put actionable data to work. This can be done with the help of products that now focus on machine learning and predictive analytics. Many of these products allow a business to view all the data in a single pane, making IT’s job exponentially easier and more strategic in terms of data analytics. IT professionals who work hard to capture and analyze the data are better prepared for the future of BI, which will no doubt intersect with AI, IoT, Cloud and business apps, reshaping the IT landscape as we know it.

Not asking for help. Building an EDW is not easy, or cheap. For the majority of technology companies, it is not their core competency. A major challenge for us in the initial stage was determining what we should build ourselves and what we should outsource. Originally, we tried to build more internally and, in all honesty, the outcome wasn’t good. While the ROI is not always easy to see, outsourcing helped us get our ducks in a row. As a bonus, our consultants were able to help analyze our business issues and needs from an outside perspective, offering insights we otherwise wouldn’t have seen. A win-win in my perspective.

Not planning for the future. BI is already moving at a rapid pace. Predictive analytics are going to become ever more important for the IT department, becoming any successful business’ Holy Grail. The company that leverages data and knows the right answers to key customer questions before they even ask will have a competitive advantage. Predictive analytics allow IT to take action in real time or even ahead of time, making their role even more strategic. Predictive analytics force the business to identify the most important KPIs to run the business and drive attention there.

Propelled forward by the massive growth of data throughout the world, the business intelligence market is expected to grow to $22.8 billion by 2020 as reported by Gartner. I highly doubt that there is a company today that is unaware of how data — and the strategic use and analysis of that data — can revolutionize their business and bottom line. While it's a challenging and time-consuming process, the creation of a fine-tuned EDW, stocked with the right data will no doubt ease management, empower the business, and enhance the customer experience for years to come.

As Lifesize CTO, Bobby Beckmann leads a multinational team of engineers and developers to deliver continued innovation, scalability and reliability to the Lifesize cloud-based software service, HD camera and phone systems. With more than 20 years of experience, Beckmann helps Lifesize build on its reputation for innovations, recent momentum and usher in the next chapter of the company’s innovation.

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