Digital Transformation in Community Banking Webinar
Digital Transformation in Community Banking
With the help of AI technologies, community banks have successfully been able to strengthen relationships, reduce churn, increase deposits, and improve ROI enterprise-wide by utilizing the data they already have at their fingertips—and your organization can, too. However, successful digital transformation can be a major challenge for midsized and community-based banks. Adopting new technologies, shifting operational mindsets, and hiring the talent necessary to build and execute upon AI solutions is a major endeavor. Many banks do not even know where to begin. In this presentation, discuss the steps and considerations for aggregating data across your bank to create a 360-degree view of your customers, why that is important, and ways other community banks have found success through analytics initiatives.
A Data Scientist's Thoughts on Artificial Intelligence, Business, and the Future
A Data Scientist's Thoughts on Artificial Intelligence, Business, and the Future
In this interview, David Cieslak, PhD, the Chief Data Scientist at Aunalytics, describes complex analytics concepts such as artificial intelligence, machine learning, and deep learning, and explains how they are useful for businesses today—and will continue to be in the future. David has been with Aunalytics since its inception and leads its Innovation Lab in the development and delivery of complex algorithms designed to solve business problems in the manufacturing/supply chain, financial, healthcare, and media sectors.
Overcome Hiring and Talent Challenges to Get Ahead of the Competition in 2023
Overcome Hiring and Talent Challenges to Get Ahead of the Competition in 2023
Hiring and retaining staff is going to be the most difficult task facing CFOs for much of 2023. This is particularly true for IT departments. In today’s economy, highly skilled IT and data experts are a scarce and expensive resource. The mid-market organization requires another option that provides access to the right tools, resources, and support.
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Does Your Mid-Market Firm Have the Right Talent to Maximize Its Data Tech Investments?
Does Your Mid-Market Firm Have the Right Talent to Maximize Its Data Tech Investments?
Investing in digital transformation technologies can be a waste of money if your company forgets one important point. That point is, no matter how cutting edge the tech or tool may be, people are needed with specific technical expertise in order to derive true business value from these investments.
Unlike large enterprises, mid-market companies often try to find this expertise in their IT manager, hoping a jack-of-all-trades approach will take care of it. This is an unfortunate mistake, since it would require the IT manager to have unusual command over a long laundry list of duties, from data integration, ingestion, and preparation to data security, regulatory compliance, data science, and building pipelines of data ready for executive reporting from multiple cloud and on-premises environments. This is not just a tall order for a mid-market IT manager to pull off, but likely an impossible one.
At the same time, it’s unreasonable to expect that most mid-market firms can hire an entire division of data experts—who each need to be highly compensated—in order to achieve the organization’s digital transformation goals. Even if a mid-market player could afford it—which is unlikely to make economic sense—these talent resources are scarce and in high demand.
If you’re still wondering whether your IT manager’s skill set, leveraged by your in-house IT technicians, can properly run the gamut required to achieve value from your data technology investments, consider that the person in this position would need the ability to master a wide range of skill sets, from cloud architecture, database engineering, and master data management to data quality, data profiling, and data cleansing. More specifically, your IT manager would need to take on five additional specialized roles for technical talent that are critical for achieving value from data technology investments.
These roles are:
Chief Data Officer/Chief Digital Officer
A chief data officer (CDO) is focused on—you got it—data. Most mid-market companies understandably don’t have a CDO, which means they don’t have anyone who assures regulatory compliance for data handling while managing and exploiting information assets, reducing uncertainty and risk, and applying data and analytics to drive cost optimization and revenue objectives. For IT managers to fulfill a CDO role, they’d have to be equipped to bring a global perspective to company data, help their organization gain competitive advantage over peers, and manage data and analytics. They’d also need the ability to secure data, transform it into valuable business information, lead digital transformation initiatives, and use data for growth and operational efficiency.
Cloud Engineer
The primary job of a cloud engineer is to keep cloud data centers operational and secure for ecosystem users to be able to store and access their data. Cloud engineers are experts in minimizing downtime, managing access to data, managing compute and storage, and setting up cloud architectures for clients, tenants, and containers. They also monitor data center hardware, servers, networks, and communications systems for operational continuity and efficiency.
Data Security Expert
Mid-market firms also need a way to channel the talents of a data security expert, CISO, or cybersecurity director to ensure cyber-security for the company’s data. Data security experts must keep current on emerging threats while executing data security strategies to fend off and remediate attacks. This involves a wide range of duties, including working closely with the IT team to run the company’s Security Operations Center (SOC), constantly monitoring servers, networks, and workstations for security threats, and staying up to date on the changing compliance laws and regulations for the business, to name a few. While larger IT teams have bandwidth to fill cybersecurity needs inhouse, many midmarket IT teams do not have capacity for the 24/7/365 monitoring and security edits needed to thwart attacks, let alone bandwidth for executing on mitigation and response strategies needed to overcome them.
Data Engineer
A data engineer’s primary job is to prepare data for analysis or operational uses, which involves integrating data from different sources, as well as implementing and executing data profiling, cleansing, transforming, and normalizing data. Data engineers also work with data in motion and use master data management to ensure data consistency across an organization. Finally, a data engineer is your go-to technical resource for database construction and management, helping to optimize the company’s data ecosystem.
Data Scientist
It should be clear now why a mid-market IT manager should not be expected to take on these additional professional roles, but in case there’s any doubt, keep in mind that a data scientist is also needed. Data scientists develop algorithms and leverage deep learning models to analyze data with artificial intelligence and machine learning. The data scientist creates the “brain” of the data analytics solution to position it for providing accurate answers based on business information. Data scientists also mine data to find opportunities for business growth and efficiency. Ideally, the data scientist uses tools that enable non-technical business users to query data sets without having to write SQL or other code.
Master of One
If you’ve correctly determined that your mid-market IT department does not have enough time to absorb these data roles into their regular duties of keeping your company systems stable and responding to help desk tickets from your team, don’t despair. There’s a viable solution for mid-market businesses with this dilemma: they can partner with data experts who provide a side-by-side model coupling technology with talent. This allows the mid-market to efficiently compete, leveraging the necessary skillsets to achieve digital transformation success.
What does successful mid-market digital transformation require? The key is to have a cloud-based data center, a cloud native data management platform, and cloud native analytics, thus shifting the burden of procuring and maintaining the infrastructure to a third-party vendor in the data industry. Instead of attempting to reinvent the wheel in house, mid-market players should ensure they’re partnered with the right infrastructure to maximize the data-center capabilities, and data storage and management, for effective digital transformation.
Mid-market firms can gain the benefits of working with a wide range of experts including cloud engineers, data engineers, security experts, data scientists, and other highly skilled technical resources if they establish a partnership with a data platform company. By opting for this type of side-by-side expert help, the mid-market can achieve true business value—without needing to hire an entire data team.
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Why You Need Fresh Insights: Don’t Rely on Stale Data to Make Important Decisions - PDF
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Unlocking the Value of Data Analytics: What Mid-Market Companies Need to Understand
Unlocking the Value of Data Analytics: What Mid-Market Companies Need to Understand
Most mid-market companies make one mistake or another when investing in a data analytics platform, not understanding the many intricacies associated with preparing their data to get the best results. Some of the most common mistakes include:
- Not realizing they need to build pipelines to get the data from their multiple data sources to the analytics platform
- Tasking IT with implementing a data analytics solution, when the IT department does not have data science skillsets
- Basing analytics on data that is riddled with errors, incomplete, or stale, which compromises quality of decision-making due to the inaccuracy and tardiness of the underlying data.
- Relying on the reporting function of one data source and not taking into account data beyond that source for decision-making
- Using dashboards that provide insights into the past only, and not the future – a gap that needs to be bridged to compete with larger enterprises
It should also be noted that analytics requires massive storage and compute to mine data for actionable insights. Even in a cloud environment, which is less costly to maintain than on-premise servers, data analytics takes up a huge amount of compute to mine transactional data for AI-driven insights. Most data warehouses used by mid-market companies are not built for analytics, and their contracts with public cloud vendors for data storage often incur huge overage charges for compute spikes as millions of calculations are being completed for algorithms to converge for analytics results. Data analytics needs a cloud built for analytics. The mid-market should demand a built-in analytics cloud from an analytics solution, without a third-party public cloud contract to make it work (or attempt to host it in their regular institution data storage).
Industry Knowledge is Key
One of the most important aspects to understand is that there is no one-size-fits-all when it comes to data analytics. The value lies in industry specific data models, which must be built with algorithms using salient data points for a specific industry and appropriately weighted for that industry. For example, to build customer revenue in financial services, mining transactional banking data is important to reveal if a customer is doing business with competing financial institutions so that action can be taken to win this business over. In manufacturing, comparing product inventory at various channels and channel or retail sales locations is important for discerning sales performance and growth opportunities. In healthcare, mining insurance reimbursement claims for underpayments to recapture lost revenue requires comparisons of contracted amounts and fee schedules for multiple private insurance companies, plan coverages, and more. The true business value of analytics lies in industry specific data models and the data enrichment made possible by deep learning and the generation of actionable insights.
The Importance of Having the Right Expertise
This brings us to the requirement for data scientists and business analysts, which assist with achieving powerful and current actionable insights that lead to AI-driven decision-making and better business outcomes. Data scientists build algorithms to detect trends, patterns and predictions based upon the data, to position an enterprise for the future. Business analysts are industry specific and connect the dots between the data points relevant for answering business questions in that particular industry. They also help to design dashboards and other data visualizations to ensure that the insights generated answer questions important to that industry using industry-specific terminology, and analyze analytics results in the context of industry knowledge to reveal growth drivers and other opportunities for driving revenue. Typical IT departments do not have these skill sets.
Data Analytics Platform Requirements
Consider these questions when evaluating a data analytics platform:
- Which data sources are forming the basis for the insights: Is the analysis based upon only some of the data, leaving out important data sources? Do the analytics use the most important data points? Is too much data or the wrong data being used?
- How is the data cleansed for accuracy: To judge the accuracy of the insights, know what is being done to eliminate errors in the underlying data being analyzed. Garbage in leads to exponential garbage out when data is turned over to AI. Can the data be trusted?
- Which algorithms are being used to find insights: Is it a specialized or generic deep learning model? Is it optimized for the type of inquiry or result being sought? Is it tailored to a specific industry and weighted appropriately for that industry and the question posed? Can the results be trusted?
- Are the analytics results providing actionable insights such as how to grow revenue, improve efficiency or achieve other business outcomes? Are the results tied to solving business challenges, and not just AI for the sake of being cool?
Side-by-Side Approach
Most mid-market companies are not in the business of IT or data science. IT is a necessary administrative function for operating a business, but should not become the main focus of most non-tech mid-market companies. However, given the expertise needed to use and maintain most analytics solutions, and the cost of data expert professionals as FTEs, the cost of data management and analytics can quickly overtake mainline business COGS expenses. Given all the complexities and challenges associated with unlocking the true value of data analytics, a new approach is needed that mid-market businesses can afford, enabling them to leverage AI-driven analytics and more effectively compete.
A side-by-side service model offers an alternative that goes beyond most tools and platforms on the market by providing a data platform with built-in data management and analytics, as well as access to human intelligence in data engineering, machine learning, and business analytics. Ideally it should operate as a cloud-based platform with a subscription service that places the burden of the data engineering expertise, technical tooling, and building and maintaining the infrastructure on the data management provider.
While many companies offer tools, and many consulting firms can provide guidance in choosing and implementing the tools, integration of all the tools and expertise in one end-to-end solution built for non-technical business users is key for digital transformation success for midmarket businesses.
