Our CEO was recently part of a digital technology roundtable about big data, hosted by Crain's Chicago Business. These are his answers to questions that many in the analytics and BI industry often consider.
“Since our founding, Analytics8 has been helping our clients transform data into knowledge. Our typical engagement starts with a broad assessment of an organization’s data needs and challenges. We typically find multiple data silos and overlapping data efforts at various levels of quality and completeness. We help clients prioritize their needs and get them on the right path quickly – and that means different things for different clients.
Most of our clients have various legacy databases, and a key objective we commonly see is the desire to move to a modern data architecture. That often means moving more data assets to the cloud via software hosted on AWS or Azure or helping them manage a hybrid cloud deployment. Our consultants work with each client to provide guidance on how to best use their data to solve their unique business problems.”
“We preach to our consultants and clients, “organizations that effectively use their data assets will “win” and those that don’t will lose to their competition. The stakes are incredibly high. Businesses should start by defining their overall strategic mission and departmental objectives to then figure out how data and analytics can support each goal. Through better use of data, we’ve seen incredible transformation across so many industries: retailers improve relationships with franchises, manufacturers improve product quality and reduce worker injuries, asset management companies increase returns, insurance companies reduce risk for their clients, and even fire departments save more lives.
The key is starting with the big picture, figuring out where the data lives, and then plotting an efficient course to tackle objectives one at a time. Working with a partner that has “been there and done that” can keep organizations from wasting time and going astray.”
“One of our more recent data solutions is one we implemented for Crow Wing County, MN. Their major objective was and is to reduce criminal recidivism, or the tendency for a convicted criminal to re-offend. Discharge plans were created for offenders, but the one-size-fits-all approach wasn’t optimal. By analyzing their data sources, including external data, standardizing and cleansing the data, and then applying various analytics techniques, the County can now easily gather all the information needed to create customized and informed discharge plans on an individual basis. With this data solution, the County is equipped to reduce crime, resulting in a safer community, better lives for ex-cons, and better allocation of county funds.”
“In order to stay competitive, organizations must be able to use and analyze all of their data, regardless of its size. This includes data from traditional data sources (their own financial, customer, or other internal systems) along with newer sources of data such as IOT, social media, and other external data, both public and proprietary. “Big Data” at one point meant a non-traditional (Hadoop) database or storage in a data lake as opposed to a data warehouse. The software landscape is moving so fast that these distinctions mean less and less over time. We help clients cut through the noise and focus on the fundamentals of managing data of all sizes while taking advantage of the cutting-edge technologies available.”
“A modern data strategy addresses more than the data; it is a roadmap that defines People, Process, and Technology. A good strategy starts with clear and concise goals. Outline the key challenges you want to overcome and the business critical questions that need answering. Then, you need to figure out what data is needed to accomplish your goals and how will you source the data. Next, define your infrastructure requirements, such as data integration, data storage, privacy, and security. Your data infrastructure should accommodate current requirements but be built to handle future needs. Then, choose a toolset that will help you turn data into insight, and ensure your people have the right skills and that organizational process encourage data-driven behaviors.
A documented roadmap is critical to your data strategy. A roadmap serves as a playbook for how and when everything will be accomplished, including technical details and an overall timeline.”
“Right now, one of the biggest challenges is “who is responsible for our organization’s data assets?” Historically, that responsibility has been somewhere in the bowels of the IT hierarchy. Some forward-thinking companies have a Chief Data Officer, but even with a CDO, responsibilities between a CIO and CDO are not always clear. There is also a convergence of traditional business intelligence and AI/machine learning coming from completely different areas within an organization. Most large organizations have a centralized IT-sanctioned process to access and use data along with multiple decentralized pockets of data and analytics efforts. Many in “big data” are trying to gain control of their organization’s data and analytics assets while still providing flexibility and easy access to those that need it – and it starts with “who’s responsible?” ”
“Outsourcing, in terms of hiring external experts to help, is probably the number one thing an organization can do to get on the right track with achieving success with their data. Working with someone who has “been there and done that” can save millions of dollars and months or years of effort.
Offshoring, in an attempt to save on labor costs, is one of the worst things an organization can do, at least when it comes to strategy or project work. We have found that working directly with our clients, face-to-face, to develop, design, and implement data and analytics solutions is far more efficient and effective at long term problem-solving. However, offshoring can be a good way to handle support and maintenance efforts.”
“Nearly all of the software and infrastructure used for “big data” can be set up in a secure way, even on the cloud. The biggest problems with security are the people and processes that a company adopts. We adopt several safeguards for our consultants when they work with our clients’ data, including a prohibition of hosting client data outside of our clients’ environment, 3rd-party security audits, an adoption of GDPR standards, even for US clients, encrypted everything, a no-thumb-drive policy, and specific anti-phishing policies. As a first step, we recommend that our clients make data and network security one of the top priorities of a CIO, and that the CIO has the authority and resources to implement the right systems, tools, processes, and hire the right people.”
“The most important thing for organizations to do when faced with figuring out which technology innovations to adopt is to take a step back and look at overall objectives, starting with the corporate mission and then the tactical activities that support the mission. It is easy to get dazzled by cool tech and a good sales pitch. In fact, big data as a concept has already led companies down dead-end paths. A few years ago, big data was almost a synonym for Hadoop. Hadoop was and is a great technology for some things, but a lot of the companies that went “all in” on Hadoop are now figuring out how to get all their data onto a cloud-based big data platform like Snowflake or Amazon Redshift. Companies that stay focused on supporting business objectives with the correct technology will thrive and survive. Those that chase new tech for tech sake will not.”
“A few years ago, we saw lots of companies starting to store huge amounts of data without much of an idea about what to do with it: log files, IOT data, social media data, geospatial data, etc. The traditional business intelligence model of modeling, ETL, and warehousing didn’t quite work – both for technical reasons and because nobody really knew what they actually wanted to do with that data. With accessible machine learning, companies can now augment their human analysts with machines and actually make use of that vast amount of data sitting in a data lake. We’ve seen clients introduce machine learning to their data which allowed them shift from traditional analytics (looking at why something happened) to utilizing more predictive analytics to take control of a situation before it happens. The skills needed to make use of this technology are different (but still accessible to most data-savvy professionals).”
“All industries can benefit in fundamental ways by using and analyzing more data. Right now, financial services companies as a whole seem to us their data in new and innovative ways. We’re also seeing big improvements in the way companies handle distribution and logistics data. Retailers often have the most to lose in not maximizing the value from their data because of the disruption from Amazon. We see huge opportunities in healthcare, but that industry tends to be more conservative. No matter what industry a company is in, they have an opportunity to use their data to get ahead of competitors and offer better customer service.”
“We’d certainly like them to know that Analytics8 is here to help them tackle their big data challenges. But even if we’re not their partner of choice, we want them to know that having a trusted data and analytics partner is key to technical implementations but also staying well-informed of the big data landscape. Let the vendors show you their stuff, but find someone that provides guidance and can help you cut through the noise to get on the right path quickly.”
Access the full roundtable on the Crain’s website here.
Mr. Fussichen is the president of Analytics8. His started his professional career at MicroStrategy as a consultant and then took multiple positions at Business Objects (since acquired by SAP). He started Analytics8 in the US in 2005 and has led Analytics8 for the past 14 years. He has a BS in Industrial Engineering from Ohio State.
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