Perficient’s vibrant culture is fueled by leaders who bring sharp thinking, deep expertise, and a collaborative spirit to their day-to-day work. We recently connected with John Vylasek, senior solutions architect and data strategist, whose journey from military intelligence and commodity trading to client strategy has shaped his bold, visionary approach to problem-solving.  

John currently leads strategic data efforts at Perficient, using AI, analytics, and deep business knowledge to help clients drive impact. In this People of Perficient profile, we’ll explore how John’s diverse background and passion for innovation empower Perficient to deliver transformative, AI-first solutions with purpose and impact. 

What is your role? Describe a typical day in the life.    

As a data strategist, I help solve a wide range of client challenges. My day usually starts with reviewing my plan from the previous day while also checking emails and Teams for any new adjustments. I’m currently engaged full-time as Data Delivery Director with a major global financial services client, where things can shift quickly due to regulatory and business requirement changes. Thankfully, my team is always responsive and able to adapt as needed, which makes Perficient a great strategic partner. 

How did your experience in the military shape your approach to leadership? 

My time in military intelligence taught me how to break down complex problems, communicate clearly up the chain of command, and lead with both confidence and humility. I was asked at the age of 19 to assemble a team of intelligence analysts with the skills required to tackle an important and ambiguous challenge. We developed effective new techniques, and just months before Operation Desert Storm began, we applied our solutions to great effect. That experience taught me the value of building the right team, being bold yet respectful, and focusing on meaningful impact. One of the biggest lessons I carry is to “just make it happen”—a mindset I apply daily, whether with clients or internal teams. 

What brought you to Perficient, and how do your past experiences align with your current role? 

I joined Perficient while consulting independently, thanks to a referral, and it just clicked. My background in tech, leadership, analytics, and decomplicating ambiguity makes data strategy a great fit. I’m still coding, diagramming, translating complexity into clarity, and diving headfirst into new challenges. The consulting side was new to me at this scale, but because I’ve solved similar problems before, I adjusted quickly. I love helping clients align, adapt, and problem-solve with confidence, and I get genuinely excited when a new curveball comes my way. 

Whether big or small, how do you make a difference for our clients, colleagues, communities, or teams? 

I make a difference by asking many questions. It might seem small, but it helps the entire team learn and speak up. I often hear, “I was wondering the same thing,” which creates a more open, collaborative environment. I also love sharing resources that have helped me grow, like Cloud Data Architectures Demystified or helpful Udemy courses. As a lifelong learner, I’m always generous when passing along what’s worked for me. 

READ MORE: Hear From Our Colleagues About How They Prioritize Learning and Development 

Whether it’s creating a quick diagram during a meeting or reframing a problem in clearer terms, I break things down into understandable chunks so people can understand our goals and act on next steps with confidence. I take the same approach in client discovery—asking leading questions, listening closely, and creating a safe space for open and transparent communication. Being humble and approachable makes it easier for others to do the same, and that’s where real progress happens. 

What was the most rewarding part about serving as a mentor in the Mark Cuban AI Bootcamp? 

The most rewarding part of mentoring in the Mark Cuban AI Bootcamp was watching a group of bright high school students come together, collaborate, and build something meaningful. With some light coaching on inclusion and teamwork, they quickly aligned and focused on a shared goal: using AI to help people in the physical world.  

We developed a gesture recognition model to help people with communication challenges express specific needs through custom-trained motions, even when away from familiar caregivers. It was powerful to see how that idea took shape through trial, iteration, and collaboration. 

By the end of the program, not only did our model work, but my students also won the final “Shark Tank” pitch for the most impactful idea. One of them said, “We never would have gotten here if everyone didn’t think differently,” which really stuck with me. That diversity of thought, and the chance to help guide it, was incredibly rewarding. Hearing later that one student had highlighted her experience working with me as a key takeaway from the program made it even more meaningful. 

READ MORE: Perficient’s Award-Winning Partnership With the Mark Cuban Foundation 

What advice would you give to colleagues who are just starting their career with Perficient?

My first piece of advice is simple: handle your basics. Get your timesheets in, complete your training, and manage your time like a professional while always learning and improving yourself. It may seem small and obvious, but it sets the tone for everything else you do. 

Second, understand that how you show up internally at Perficient may need to be different than how you show up with a client. With clients, you lead through collaboration and patience, bringing people along at their own pace. Internally, especially during pursuits, business moves fast, and it helps to be more direct and decisive. Know your role, understand who’s leading, and stay aligned. If you have a concern, think about whether it’s the right time to raise it. I’ve coached and mentored people on this—sometimes it’s better to hold off on a small technical rabbit hole detail rather than disrupt momentum when the group is already aligned. I’m still learning and adapting myself, but this distinction has been key to working effectively. 

Why are you #ProudlyPerficient?    

I’m #ProudlyPerficient because I get to work alongside sharp, highly adaptive people who are always ready to dive in and get things done. Internally, there’s a fast pace and a bias toward action, so I’ve learned that often you need to step up, assign roles, and lead decisively. It’s not about being the loudest voice in the room; it’s about clarity, support, and knowing when to speak up and when to stay focused on the goal. That kind of teamwork and trust is what gets stuff done. 

I appreciate how differently we show up for clients—collaborative, patient, and meeting them where they are. That flexibility between both modes while staying grounded in the work is what makes Perficient special. We’re not just delivering solutions; we’re building alignment. I’m proud to be a part of that. 

How has collaborating with our global teams shaped your growth journey at Perficient?
I’ve been leading global teams for many years, and the approach is consistent—find the people who make the extra effort to communicate, align, and get things done. Whether they’re in Latin America, India, or elsewhere, those relationships are what get “it” done. Building that network, finding your go-to experts, and recognizing talent across borders have been the most rewarding parts of my journey. 

LEARN MORE: Perficient’s Global Footprint Enables Genuine Connection and Collaboration 

How does staying up to date with evolving technologies help you better serve clients?   

One of my goals is to deepen my understanding of how to use local large language models (LLMs) in secure, practical ways. It’s an incredible accelerator for learning and staying up to speed. With a manufacturing client, I used an LLM to help map two complex database schemas. By feeding in just the field names and a few sample rows, the model was able to do most of the heavy lifting in identifying how the old system aligned with the new one. It wasn’t perfect, but it saved a lot of time and gave us a strong head start. Continuing to explore how AI can support data strategy and problem-solving is a key part of my growth path. 

What does being an AI-first company mean to you?
To me, being an AI-first company means starting from a place where AI is always considered not only as a solution itself but also as an accelerator to identify the solution and the steps to get there. It is a new way of thinking that saves us time and our clients’ money.  

 At Perficient, we approach AI with purpose and lead conversations when it makes sense to lead with it and when it does not. That level of thoughtfulness is part of what sets us apart. 

LEARN MORE: How We Are Building an AI-First Enterprise 

What are you passionate about outside of work?   

Outside of work, I spend a lot of time with my family. My oldest son lives just four doors down, and we’re often outside fishing with the grandkids. I also support my wife, who went from being a stay-at-home mom to now serving as Dean of the School of Health Sciences over several programs. I’m the primary cook at home and like making healthy meals. I’m also into photography, especially aurora and space photography. I’ve been able to get some great shots even from our backyard in the city. Staying active is also a big focus of mine, so we spend a lot of time out in nature. 

SEE MORE PEOPLE OF PERFICIENT  

It’s no secret our success is because of our people. No matter the technology or time zone, our colleagues are committed to delivering innovative, end-to-end digital solutions for the world’s most innovative companies, and we bring a collaborative spirit to every interaction. We’re always seeking the best and brightest to work with us. Join our team and experience a culture that challenges, champions, and celebrates our people.  

Learn more about what it’s like to work at Perficient at our Careers page. See open jobs or join our talent community for career tips, job openings, company updates, and more!  

Go inside Life at Perficient and connect with us on LinkedIn, YouTube, X, Facebook, and Instagram. 

I’ve been coding in notebooks for so long, I forgot how much I missed a nice, deployed application. I also didn’t realize how this was limiting my solution space. Then I started working with Databricks Apps.

Databricks Apps are designed to extend the functionality of the Databricks platform, providing users with enriched features and capabilities tailored to specific data needs. These apps can significantly enhance the data processing and analysis experience, offering bespoke solutions to address complex business requirements.

Key Features of Databricks Apps

1. Custom Solutions for Diverse Needs: Databricks Apps are built to cater to a wide range of use cases, from data transformation and orchestration to predictive analytics and AI-based insights. This versatility allows organizations to deploy applications that directly align with their specific business objectives.
2. Seamless Integration: The apps integrate smoothly within the existing Databricks environment, maintaining the platform’s renowned ease of use and ensuring that deployment does not disrupt current data processes. This seamless integration is crucial for maintaining operational efficiency and minimizing transition challenges.
3. Scalability and Flexibility: Databricks Apps are designed to scale with your organization’s needs, ensuring that as your data requirements grow, the solutions deployed through these apps can expand to meet those demands without compromising performance.
4. Enhanced Collaboration: By leveraging apps that foster collaboration, teams can work more effectively across different departments, sharing insights and aligning strategic goals with more precision and cohesion.

Benefits for Architects

1. Tailored Data Solutions: Databricks Apps enables architects to deploy tailored solutions that meet their unique data challenges, ensuring that technical capabilities are closely aligned with strategic business goals.
2. Accelerated Analytics Workflow: By using specialized apps, organizations can significantly speed up their data analytics workflows, leading to faster insights and more agile decision-making processes, essential in today’s fast-paced business environment.
3. Cost Efficiency: The capability to integrate custom-built apps reduces the need for additional third-party tools, potentially lowering overall costs and simplifying vendor management.
4. Future-Proofing Data Strategies: With the rapid evolution of technology, having access to a continuously expanding library of Databricks Apps helps organizations stay ahead of trends and adapt swiftly to new data opportunities and challenges.

Strategies for Effectively Leveraging Databricks Apps

To maximize the potential of Databricks Apps, CIOs and CDOs should consider the following approaches:

• Identify Specific Use Cases: Before adopting new apps, identify the specific data operations and challenges your organization is facing. This targeted approach ensures that the apps you choose provide the most value.
• Engage with App Developers: Collaborate with app developers who specialize in delivering comprehensive solutions tailored to your industry. Their expertise can enhance the implementation process and provide insights into best practices.
• Promote Cross-Department Collaboration: Encourage departments across your organization to utilize these apps collaboratively. The synergistic use of advanced data solutions can drive more insightful analyses and foster a unified strategic direction.
• Assess ROI Regularly: Continuously assess the return on investment from using Databricks Apps. This evaluation will help in determining their effectiveness and in making data-driven decisions regarding future app deployments.

Conclusion

Databricks Apps present a powerful opportunity for CIOs and CDOs to refine and advance their data strategies by offering tailored, scalable, and integrated solutions. By embracing these tools, organizations can transform their data-driven operations to gain a competitive edge in an increasingly complex business landscape.

Contact us to learn more about how to empower your teams with the right tools, processes, and training to unlock Databricks’ full potential across your enterprise.

Asking questions about data has been part of Oracle Analytics through the homepage search bar for several years now. It did that with Natural Language Processing (NLP) to respond to questions with various automatically generated visualizations. What has been introduced since late 2024 is the capability to leverage Large Language Models (LLM) to respond to user questions and commands from within a Workbook. This brings a much-enhanced experience, thanks to the evolution of language processing from classic NLP models to LLMs. The newer feature is the AI Assistant, and while it was earlier only available to larger OAC deployments, with the May 2025 update, it has now been made available to all OAC instances!

If you’re considering a solution that leverages Gen AI for data analytics, the AI Assistant is a good fit for enterprise-wide deployments. I will explain why.

• Leverages an enterprise semantic layer: What I like most about how AI Assistant works is that it reuses the same data model and metadata that are already in place and caters for various types of reporting and analytical needs. AI Assistant adds another channel for user interaction with data, without the risks of data and metadata redundancy. As a result, no matter whether creating reports manually or leveraging AI, everyone across the organization remains consistent in using the same KPI definitions, the same entity relationships and the same dimensional rollup structures for reporting.
• Data Governance: This is along the same lines as my first point, but I want to stress the importance of controls when it comes to bringing the power of LLMs to data. There are many ways of leveraging Gen AI with data and some are native to the data management platforms themselves. However, implementing Gen AI data querying solutions directly within the data layer requires a closer look at security aspects of the implementation. Who will be able to get answers on certain topics? And if the topic is applicable to the one asking, how much information are they allowed to know?

The AI Assistant simply follows the same object and row level security controls that are enforced by the semantic data model.

• What about agility? Yes, governed analytics is very important. But how can people innovate and explore more effective solutions to business challenges without the ability to interact with the data that comes along with these challenges. The AI Assistant works not only with the common enterprise data model, but with individually prepared data sets as well. As a result, the same AI interface caters to questions asked about both enterprise data as well as departmental or individualized data sets.
• Tunability and Flexibility: Enabling the AI Assistant for organizational data, while relatively an easy task, does allow for a tailored setup. The purpose of tuning the setup is to increase the levels of reliability and accuracy. The flexibility comes into play when directing the LLM on what information to take into consideration when generating responses. And this can be done through a fine-tuning mechanism of designating which data entities and/or fields of data within these entities, can be considered.
• Support for data indexing, in addition to metadata: When tuning the AI Assistant setup, three options are available to pick from, down to the field level: Don’t Index, Index Metadata Only, and Index. With the Index option, we can include information about the actual data in a particular field so the AI Assistant is aware of that information. This can be useful, for example, for a Project Type field so the LLM is informed of the various possible values for Project Type. Consequently, the AI Assistant provides more relevant responses to questions that include specific project types as part of the prompt.
• Which LLM to use? LLMs continue to evolve, and it seems that there will always be a better, more efficient and more accurate LLM to switch to. Oracle has made the setup for the AI Assistant open, to an extent, in that it can accommodate external LLMs, besides the built-in LLM that is deployed and managed by Oracle. At this time, if not using the built-in LLM, we have the option of using an Open AI model via the Open AI API. Why may you want to use the built-in LLM vs an Open AI model?
  • The embedded LLM is focused on the analytical data that is part of your environment. So it’s more accurate in that it is less prone to hallucinations. However, this approach doesn’t provide flexibility in terms of access to external knowledge.
  • External LLMs include public knowledge (depending on what knowledge an LLM is trained on) in addition to the analytical data that is specific to your environment. This normally allows AI Assistant to have better responses when the questions asked are broad and require public knowledge to tie into the specific data elements housed in one system. Think for example about geographical facts, statistics, weather, business corporations’ information, etc. These are public information and can help in responding to analytical questions within the context of an organization’s data.
  • If the intent is to use an LLM but avoid the inclusion of external knowledge when generating responses, there is the option to restrict the LLM so it limits responses based on organizational data only. This approach leverages the reasoning capabilities of models without compromising the source of information for the responses.
• The Human Factor: AI Assistant factors in the human aspect of leveraging LLMs for analytics. Having a conversation with data through natural language is to the most part straight forward when dealing with less complex data sets. This is because, in the case, the responses are more deterministic. As the data model gets more complex, there will be more opportunities for misunderstanding and missed connections between what’s on one’s mind versus an AI generated response, let alone a visual one. This is why the AI Assistant has the capability for an end user to adjust the responses to better align with their preferences, without reiterating prompts and elongated back and forth conversations. These adjustments can be easily applied with button clicks, for example to change a visual appearance or change/add a filter or column, all within a chat window. And whatever visualizations the AI Assistant produces, can be added to a dashboard for further adjustments and future reference.

In the next post, I will mention a few things to watch out for when implementing AI Assistant. I will also demo what it looks like to use AI Assistant for project management.

The financial services sector is experiencing transformative changes driven by technological advancements and innovative trends. We are witnessing the integration of AI, the rise of hyper-personalization, and the adoption of advanced digital platforms, all of which are revolutionizing operations and client interactions. Additionally, the emergence of embedded finance and an increased focus on regulatory compliance are compelling financial institutions to continuously adapt and innovate.

Our experts have identified the most impactful trends across banking, wealth and asset management, and payments. This blog brings together these insights, presenting the top financial services trends for 2025.

Financial Services Trend #1: AI Transforming the Future of Finance

Artificial intelligence (AI) is revolutionizing the financial services industry, driving significant advancements across banking, wealth and asset management, payments, and beyond. In 2025, AI will be a cornerstone of innovation, transforming customer interactions, enhancing operational efficiency, and fostering a more inclusive and sustainable financial ecosystem. The integration of AI is reshaping the landscape by addressing challenges such as data protection, regulatory compliance, and the modernization of legacy systems.

Recommended Approach: To fully harness the potential of AI, financial institutions should prioritize improving their data strategy, ensuring high-quality, reliable, and trustworthy data. Leveraging advanced data analytics, AI, and machine learning can provide real-time insights into customer preferences, behaviors, and financial needs, creating highly individualized experiences that improve engagement and loyalty. AI-powered chatbots can handle routine inquiries, freeing human agents for complex issues, while AI-driven algorithms enhance fraud detection and risk management.

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Financial Services Trend #2: The Rise of Hyper-Personalization

In today’s fast-paced world, customers expect financial services to be as unique as their individual needs and preferences. Hyper-personalization is revolutionizing the industry by shifting the focus from traditional models to a customer-centric approach, significantly enhancing satisfaction and retention rates. Advancements in data analytics, AI, and machine learning, enable financial institutions to offer highly personalized services. The transformation is further accelerated by shifting client demographics, rising expectations, and competitive pressure from tech-driven players and financial product innovation.

Recommended Approach: Employing augmented analytics for predictive insights and personalized financial strategies helps anticipate customer needs and proactively offer solutions. User research and behavioral analytics can define and optimize the user journey, supporting meaningful hyper-personalization at each touchpoint. Utilizing cloud computing and modern data integrations for scalable data management and real-time collaboration, along with advanced visualization tools and API integrations with third-party data, enables financial institutions to isolate priority scenarios and accelerate deeply tailored outreach. This comprehensive approach will be crucial for attracting and retaining customers, fostering loyalty, deepening engagement, and driving long-term growth.

Success In Action: Elevating ESG in Wealth Management Portfolios

Financial Services Trend #3: Embedded Finance Boosts Revenue and Reach

Embedded finance is transforming the financial services landscape by integrating financial products directly into non-financial platforms. Driven by the demand for faster transactions and the need for businesses to offer comprehensive financial services within their existing ecosystems, various industries are embedding payment, lending, insurance, and investment options into their platforms. This integration not only enhances customer experience but also opens new revenue streams and market opportunities for financial institutions.

Recommended Approach: To capitalize on the rise of embedded finance, financial institutions should focus on several key strategies. First, they should form strategic partnerships with fintech companies and non-financial platforms to extend their reach and integrate their services effectively. Investing in technology, including upgrading IT infrastructure to support embedded finance and adopting cloud-based solutions, is crucial. Developing robust APIs will enable seamless integration of financial services into third-party platforms, ensuring security, reliability, and ease of use. By adopting these strategies, financial institutions can unlock new revenue streams, expand customer reach, and enhance distribution channels.

Explore More: Turn Data into Customer Delight

Financial Services Trend #4: Leveraging Advanced Digital Platforms

The financial services industry is undergoing a significant transformation with the rise of advanced digital platforms. These platforms are reshaping how financial institutions manage payments, wealth, and client interactions. The convergence of innovative financial systems and comprehensive digital tools enables personalization, omni-channel experiences, and enhanced customer engagement. This transformation is fueled by technological advancements, regulatory changes, and increasing consumer expectations for personalized experiences.

Recommended Approach: To thrive in this evolving landscape, financial institutions should integrate advanced analytics and AI to enhance transaction success rates, fraud detection, and customer insights. Embracing mobile and cloud-based solutions will ensure flexible, anytime-anywhere access to essential data and tools. Automation can streamline administrative tasks, enabling a do-it-yourself (DIY) model for clients while allowing institutions to focus on personalized services. By modernizing legacy systems and integrating digital platforms with existing services, financial institutions can offer seamless, efficient, and personalized experiences.

Success In Action: Cloud-Native Microservices Drive Next-Generation Products

Financial Services Trend #5: Thriving Amid Regulatory Changes

The financial services industry is facing an increasingly complex regulatory environment. New rules focused on data privacy, cybersecurity, and sustainability are being implemented to ensure stability and consumer protection. These changes require significant adjustments in risk management, compliance frameworks, and operational protocols, leading to higher compliance costs and operational expenses.

Recommended Approach: To navigate these regulatory shifts, financial institutions must balance innovation with compliance. Adopting compliance technologies that automate regulatory reporting and streamline processes will help institutions stay agile. Leveraging AI and machine learning can enhance real-time transaction monitoring, fraud prevention, and compliance processes such as know your customer (KYC) and anti-money laundering (AML) checks. Investing in advanced technologies will help identify potential risks and ensure compliance with evolving regulations. By staying informed and proactive, financial institutions can mitigate risks, maintain trust with customers and regulatory authorities, and remain competitive in the dynamic financial landscape.

See Also: Strategies + Solutions to Ensure Regulatory and Compliance Excellence

Discover Your Potential With Perficient

We bring extensive financial services industry insights and end-to-end digital expertise to accelerate efficiencies and power data for differentiated, trust-enhancing experiences. 

• Business Transformation: Drive strategic initiatives to enhance operational efficiency, reduce costs, and foster innovation across financial institutions.
• Data + Analytics: Harness data and analytics to boost insights, accelerate decision-making, and optimize financial processes.
• Modernization: Leverage modern technology and architecture to drive integrated, transformative financial solutions.
• Risk + Compliance: Control risk, meet regulations, and stay ahead of financial industry changes.
• Consumer Experience: Enhance customer journeys through personalized, seamless, and engaging digital financial service experiences.

We are trusted by leading technology partners and mentioned by analysts. Discover why we have been trusted by 18 of the 20 largest commercial banks, 16 of the 20 largest wealth management firms, and 25+ leading payments and card processing companies. Explore our financial services expertise and contact us to learn more.

Data plays an essential role in today’s digital economy and keeping up with modern data management processes is key to staying competitive. In fact, enterprises with advanced data practices are more productive, innovate faster, are able to enter new markets quickly, and are more likely to be directly monetizing their data compared to their less-mature peers. But achieving that level of competency often requires the assistance of a data management service provider.

Partnering with a data management service provider can help your organization:

• Establish a strategy and operating model for data
• Build an enterprise data foundation
• Mature and scale data governance

In the Now Tech: Data Management Service Providers, Q4 2021 report, Forrester defines data management service providers as:

“Service firms that provide talent, technology, and best practices through strategy and deployment partnerships in order to improve an enterprise’s use of data management to drive insights and business results.”

Forrester Now Tech: Data Management Service Providers, Q4 2021 Report

Identifying the right service provider to partner with can help you realize the benefits of data and increase your data competency.

In the report, Forrester segmented vendors based on market presence and functionality. Market presence was determined by data management service revenue and vendors were placed into one of three categories: large established players, midsize players, and small players. Functionality was based on varying capabilities and broken down into four segments: platform providers, data and analytics services, specialized service providers, and system integrators.

Each vendor was asked a number of detailed questions about their services, including geographic presence, industry expertise, and data management experience and expertise. Based on the responses, Forrester supplies information about these service providers to help you determine the best vendor for your data management needs.

Perficient’s Primary Functionality Lies in Specialized Services

Forrester listed Perficient in its midsize category ($100M to $1B in annual category revenue) as a specialized service provider. According to the report, “Specialized service providers concentrate on data and governance foundations. These firms have extensive data engineering, data management, data security, and data governance expertise for data-driven initiatives prioritized by CIOs, chief data officers, and enterprise architects. Engagements focus on data strategy, data architecture, data operations (DataOps), and data governance, helping enterprises transition into insight-driven businesses.”

Perficient’s listing in this Now Tech includes our geographic presence (100% North America); industry focus areas (healthcare and life sciences, financial services, and retail); and sample customers (Novant Health, StorageMart, and United Wholesale Mortgage).

Perficient’s Approach to Data Management

One of the greatest attributes of data is that it becomes more valuable the more you use it. But seeing that value is difficult if you’re not managing your data properly.

As a specialized service provider, we’re helping leading companies create actionable business insights based on accurate, scalable, and comprehensive data.  We bring the thought leadership, technology expertise, and processes to help our customer become data-driven organizations capable of leveraging data for competitive advantage. We do this through:

• Taking the time to understand your business
• Collecting, organizing and managing data from all over your organization
• Delivering insights via intelligent applications
• Deploying data and insights to any user via any interface

Learn More about Perficient

We’re ready to help you realize the benefits of data no matter where you are on your journey. Our experience, our technology partnerships, and most importantly our people are what make us a great partner. Visit us on Perficient.com and learn more about how we can help you master the realities of the data-driven world. And listen to the Intelligent Data Podcast where we interview thought leaders on a variety of topics around using data and technology to reshape your business.

You can read the entire Forrester Now Tech: Data Management Service Providers, Q4 2021 report via the Forrester website where it’s available to Forrester subscribers and for purchase.

MicroStrategy World 2020 may be in our rearview, but now is a great time to start taking what we learned from the conference and figuring out how we can apply it throughout the rest of the year. For instance, how we can follow the trends that industry professionals are seeing in the field of analytics.

In his “Future Trends: Driving Analytics Adoption” session at MicroStrategy World, Ventana Research CEO and Chief Research Officer Mark Smith talked about analytics trends and driving adoption. Based on what we learned, here are 5 trends you can follow to maximize analytics adoption.

By 2020, 90% of business professionals and enterprise analytics say data and analytics are key to their organization’s digital transformation initiatives. – Research and Markets

Trends Driving Analytics Adoption

Using data and analytics to drive business decisions, better customer experiences, and overall digital transformation is a goal that most organizations share, but the path to adoption comes with challenges. While modern, cloud-based analytics and end-user self-service have helped increase adoption and the value of analytics, there are a few trends you can start following to bolster your success.

1. Embrace and Use Mobile Computing

Access to mobile analytics is greatly improving adoption and providing users with data immediately. Plus the widespread deployment of 5G will likely make access even faster and accelerate the mobile-first movement furthermore.

The voice and proximity on mobile provide personalized context to information. And Mobile computing with IoT and XR also provides augmented and virtual potential.

2. Embedded Analytics Everywhere

Users no longer have to switch between tools to find the data and insights they need. Instead, analytics can be integrated into a user’s day-to-day workflow. This seamless integration, enabled by Open platform, provides both internal and external users with immediate access to actionable insights inside of the applications and processes they’re already using in a context-aware manner.

3. Ensure Intelligence in Analytics

Embracing advanced, prescriptive, and predictive analytics tools is another driving force behind adoption. These tools can generate context and drive real insights. Specifically, using the Semantic model and graph to generate context and prescriptive and predictive analytics to drive real insights.

When generating context with advanced analytics, Semantic data modeling techniques are great because they can be used to define the meaning of data within the context of its interrelationships with other data. Basically, it can define how data relates to the real world. And this is key because users are more willing to see the value in analytics if insights are accompanied by context. Without context it’s difficult to take action and users are often left with more questions than answers.

Once users have context, predictive and prescriptive analytics can guide the next steps. It’s one thing to see the data, but knowing the story and suggested prescriptive action can make all the difference. Predictive analytics provides you with the raw material for making informed decisions, while prescriptive analytics provides you with data-backed decision options that you can weigh against one another.

Insights are worth a nickel, actions are worth a dollar – Mark Smith, CEO and Chief Research Officer, Ventana Research

4. Embrace NLP and Conversational Analytics

A general lack of data literacy among non-specialist users has made adopting analytics tools challenging, but improvements with natural language processing and conversational analytics are expanding the potential user pool. These tools remove the need to program queries into an analytics tool and make it easy to query databases. Conversational computing can process a large number of conversations (text and voice) at scale in the form of natural language processing and help bring immediate insights a lot more easily.

Furthermore, Gartner predicts that 50% of analytical queries will be generated via search, voice or NLP (or automatically generated) by 2020 and that NLP and conversational analytics will drive analytics and business intelligence adoption from 35% of employees up to over 50% by 2021.

5. Utilize Collaboration to Engage People

While a lack of data literacy is a challenge, so too is a lack of data democratization. Data and analytics play a key role in an organization’s success, but there’s a lot of missed opportunities if all staff members aren’t empowered to access, analyze, and act upon the information. Allowing staff companywide to inform on strategic decisions adds to the greater overall success of your business.

To empower your employees, Forbes recommends the following:

• Share the vision across the organization
• Emphasize “soft” skills
• Establish governance
• Focus on continual learning and improvement
• Develop a “data-first” approach

Takeaways for Analytics Adoption

Data and analytics tools have the power to help create or maintain a competitive edge in your industry, but the success of your data and analytics project depends on your people. Based on the five trends above, successful user adoption comes down to:

• Are your tools fast and convenient? Users are more likely to use analytics tools if they have easy access to them (mobile) and if they can find the data quickly (embedded analytics).
• Do your users have context and insight on how to take action against data? Users want quick, actionable insights that provide background information (advanced analytics) and suggested the next steps (predictive and prescriptive analytics).
• Do your analytics tools perform the heavy lifting? Users don’t want to spend an exorbitant amount of time programming queries or sorting through complex data. Allowing NLP and conversational analytics to do the hard work not only delivers insights more easily but increases the potential user pool.
• Do your users have visibility and knowledge? Democratizing data and providing all employees with visibility into data not only increases adoption, but also more informed business decisions. But granting access to data and analytics tools also requires proper training and education.

More Insights from MicroStrategy World 2020

Check out our other MicroStrategy World 2020 blog “Too Weak, Too Slow: MicroStrategy World 2020’s Big Theme.” We discuss how MicroStrategy is addressing the idea that existing enterprise applications were built for an outdated paradigm and are too weak and too slow to meet the demands of today’s users.

“Too weak. Too slow.” That’s what Norwegian chess grandmaster Magnus Carlsen, who thinks 15-20 moves ahead, says matter-of-factly to his opponents. It’s that sentiment that MicroStrategy President and CEO Michael Saylor used during his keynote at MicroStrategy World 2020 to depict traditional and outdated IT. The tech that’s been built over the last 40 years simply doesn’t cut it anymore – it’s too weak and it’s too slow.

Computers were 1,000 times weaker 40 years ago when most of the applications organizations use were developed and simply built for a different era. Saylor said those applications are “opaque, weak, and slow.” And even though technology has greatly improved, finding answers and insights is still a cumbersome process for many.

That’s where MicroStrategy’s HyperIntelligence comes in.

HyperIntelligence 2.0 at MicroStrategy World

Organizations have numerous transactional systems and the idea of either spending time and money to re-engineer them or do a “rip and replace” simply isn’t in the budget. But there’s a third option – HyperIntelligence.

MicroStrategy rolled out HyperIntelligence in 2019, but the latest release is faster, 100 times stronger, and is designed to deliver the answer before you ask the question. HyperIntelligence 2.0 provides quick, easy solutions across all your systems and allows you to take immediate action with contextual triggers.

MicroStrategy is able to deliver these types of insights through HyperCards and HyperVision.

How to use the power of the phone, the laptop to scan forward… and answer every question before I actually click. – Michael Saylor, MicroStrategy President and CEO

HyperCards

HyperCards are atomic bytes of information that can be injected into any application that’s built to be deployed on any browser (Salesforce, Excel, Outlook, etc.). They contain predefined attributes and metrics that provide an at-a-glance summary of a specific topic on a web browser or mobile device. And these cards can cover a wide variety of topics such as people, places, things, concepts, products, or processes.

HyperCards were actually introduced in 2019, but they’ve since gotten an upgrade in version 2.0. Now users can choose from 4 different card templates. There’s basic, ring, section, and matrix. Card authors can select the card template they want to use as a starting point, and then drag and drop attributes and metrics into the pre-defined drop zones. Card authors also have the ability to mix and match header styles when building a card. This gives them the freedom to combine elements from different templates and choose the header style that best fits their data and use case.

This range of card options also gives users the flexibility to create super simple cards with information that’s only needed at a glance to more advanced cards that have profile images and widgets and can be accessed via mobile devices via HyperMobile.

HyperCard improves applications you use every day

There are a lot of applications where HyperCards would be beneficial during your workday.

Email – Users don’t have to exit their email to find answers. Instead, the answers are delivered to your email platform. For example, if a colleague emails you about a product, you could hover over the product name and a HyperCard would appear with relevant information about the product such as inventory levels.

Website – HyperIntelligence uses proprietary analytics while reading a webpage as you hover over words on the page and can deliver HyperCards with information for that page.

Search – The time it takes to research an answer can be reduced from 20 minutes down to two seconds with HyperIntelligence cards.

Calendar – Users can gain information about the people invited to a meeting on your calendar. Imagine having access to relevant insights about a customer on your mobile device before your meeting begins.

HyperVision

HyperVision was rolled out in 2019 but is also seeing updates in 2020. This application allows HyperCards to be injected into Excel spreadsheets seamlessly.

During his keynote, Saylor described a scenario where a manager had a spreadsheet with 20 plus names of employees that needed to be reviewed one by one to decipher who should be promoted, who needed training, and who should be transferred.

Instead of leaving the spreadsheet to look up information about an employee, the manager could turn HyperVision on and hover over each name for the insights needed via HyperCards. And these HyperCards can be color-coded to help filter the information in the spreadsheet. This would allow the manager in the scenario described above to filter the results in the spreadsheet to only show employees who are up for promotion for example.

Intelligence is your superpower – Hugh Owens, MicroStrategy Senior Vice President, Product Marketing

MicroStrategy Cloud

So how is all of this deployed? MicroStrategy offers its cloud platform on both AWS and Azure and allows users to switch between the two or go on-premise or off-premise. Highlights of the MicroStrategy 2020 Cloud Platform are that:

• It can be piloted in a week
• It can be deployed across your entire enterprise in a month
• You can re-engineer legacy systems or spin up a HyperIntelligence environment
• It’s a low cost, low effort, and low risk

MicroStrategy World 2020 Bottom Line

MicroStrategy 2020 is smarter, stronger and faster. Deploying HyperIntelligence is an effective way to build insights into what you’re already using. Instead of re-engineering or completely replacing what you have, you can inject intelligence into the applications you use in your everyday workflow. The insights sit on top of the data you already have and HyperIntelligence makes it faster to get the answers you need.

It was exciting to see MicroStrategy 2020 presented at MicroStrategy World this year and we look forward to what 2021 brings. If you’re looking for MicroStrategy support or a partner, drop us a line or visit our MicroStrategy page.

It’s no secret that data is a massive asset when it comes to making better business decisions. But gaining the valuable insights required to make those decisions requires quality data that you can trust. And to accomplish this you need a data strategy.

Without understanding your business objectives, identifying use cases, knowing how your users access data, and much more then you put yourself in the position of making decisions based on incomplete or incorrect insights.

Next month, leaders in the data industry will meet in New York City for the Strata Data Conference September 23-26 to share insights on how to implement a strong data strategy (as well as current hot topics like AI and machine learning, which need a strong data strategy foundation to build on).

Here are four sessions to attend to learn more about the elements of a quality data strategy.

Data Strategy Sessions at Strata

Foundations for successful data projects 
1:30pm-5:00pm, Sep 24 / 1E 10
The enterprise data management space has changed dramatically in recent years, and this has led to new challenges for organizations in creating successful data practices. Presenters, Ted Malaska and Jonathan Seidman, detail guidelines and best practices from planning to implementation based on years of experience working with companies to deliver successful data projects.

Running multidisciplinary big data workloads in the cloud 
9:00am-12:30pm, Sep 24 / 1E 14
Moving to the cloud poses challenges from re architecting to data context consistency across workloads that span multiple clusters. Presenters Jason Wang, Tony Wu, and Vinithra Varadharajan explore cloud architecture and its challenges, as well as using Cloudera Altus to build data warehousing and data engineering clusters and run workloads that share metadata between them using Cloudera SDX.

It’s not you; it’s your database: How to unlock the full potential of your operational data (sponsored by MemSQL) 
10:20am-10:25am, Sep 25 / 3E
Data is now the world’s most valuable resource, with winners and losers decided every day by how well we collect, analyze, and act on data. However, most companies struggle to unlock the full value of their data, using outdated, outmoded data infrastructure. Presenter Nikita Shamgunov examines how businesses use data, the new demands on data infrastructure, and what you should expect from your tools.

The ugly truth about making analytics actionable (sponsored by SAS) 
1:15pm-1:55pm, Sep 25 / 1A 01/02
Companies today are working to adopt data-driven mind-sets, strategies, and cultures. Yet the ugly truth is many still struggle to make analytics actionable. Presenter Diana Shaw outlines a simple, powerful, and automated solution to operationalize all types of analytics at scale. You’ll learn how to put analytics into action while providing model governance and data scalability to drive real results.

Visit Perficient’s Experts in NYC

If you’re attending the Strata Data Conference don’t forget to come visit us! Perficient is proud to be a Premier Exhibitor of the event and we’ll be at booth #1338 in the expo hall. Our experts will be onsite to strategize and showcase our expertise in complex data environments, AI, machine learning, and data strategy.

You can also connect with our team to set up a meeting, even if you’re not attending the conference. We look forward to seeing you.

The Disney Data & Analytics Conference at Disney’s Coronado Springs Resort is being held in Orlando, Florida August 20-21, 2019.  This conference introduces attendees to the tools and training needed to integrate advanced decision-making techniques into business processes that center on the experience of customers, clients, and guests. One such tool that you’ll find at the conference is MicroStrategy’s HyperIntelligence.

Join MicroStrategy experts for happy hour at The Art Garden during the conference. Details and registration HERE.

Data That Finds You

This innovative zero-click analytics tool brings data to users. Instead of flipping between dashboards, web browsers, excel sheets, email, ect. to find information, you can find what you need wherever you are in your workflow. You can simply hover over a keyword in the application you’re using to gain insights.

This technology can be integrated into Office 365 applications, Salesforce, Workday, Power BI, Slack, and web pages.

How it works

A Chrome extension surfaces information from 200+ enterprise data sources and injects real-time, contextual insights directly into a user’s browser-based workflows.

Once you download the extension, HyperIntelligence scans web pages, emails, or web applications for relevant keywords. Keywords appear underlined and users can hover over these keywords to view consolidated, bite-sized views of information called HyperIntelligence cards containing real-time insights.

HyperIntelligence Highlights

• HyperIntelligence empowers experts to deploy contextual insight to any website, screen, wall, device, or application. Get answers in real time – no interruptions, no delays to your workflow and deliver insights to users within the applications they’re already using.
• “Zero-Click Analytics” is now a reality across webpages, email clients, CRM tools, productivity platforms and Office365 applications.
• Decrease the number of users that lack access to information. Insights are pervasive and accelerate decision-making with no analytical background needed.
• Receive a Single Version of the Truth – Multiple data sources are consolidated and a robust semantic layer provide a single answer across different teams and departments.

Meet the Experts: MicroStrategy + Perficient

If you’re attending the Disney Data & Analytics Conference, you’ll have several opportunities to meet with our MicroStrategy experts and learn more about how HyperIntelligence can help you gain better business insights and make better decisions. You can find us in our partner MicroStrategy’s booth #411 sharing demos and also at a co-sponsored happy hour event at The Art Garden.

Register HERE for the happy hour event.

Where:
The Art Garden
Disney’s Coronado Springs Resort
Orlando, FL

When:
Tuesday, August 20th
5:00 pm to 8:00 pm

By now, we all know that we’ll never be able to analyze the massive amount of data that continues to grow without proper tools. So we need analytics that go further than reporting what happened and help us understand why it happened. That’s why IBM has created analytics tools that instantly transform your data into relevant insights using augmented intelligence, machine learning, pattern detection, data science and more. As an award-winning IBM partner, we’re excited for the capabilities in the all-new IBM Cognos Analytics.

Cognos Analytics 11.1 Key Features

Cognos Analytics 11.1 is easy to use even for beginners, yet powerful enough for the most demanding data explorer, and can fit virtually any budget. Key Features include:

• Storytelling
• Smart exploration
• Automated visualizations
• Reusable content
• Advanced analytics

Watch the video below for more features and to hear from one of our IBM experts, Abhi Majumdar, about how he sees Cognos Analytics 11.1 helping to empower users to make smarter decisions and gain better insights.

Majumdar, Lead Consultant and Architect, is a part of the Data Solutions team with over fourteen years of technology and business consulting experience.  He enables various clients in their data and analytics endeavors and has in-depth expertise across the full IBM Analytics stack.

“I have seen a lot of solutions in the market. I haven’t seen something which is put together in one place like this.” – Abhi Majumdar, Lead Consultant, Architect – Data Solutions Group talking IBM Cognos 11.1

The cloud is enabling healthcare organizations to drive increased flexibility in maximizing the use of their data. It is also helping to lower costs, and provide better security for a variety of data types and use cases.

Organizations no longer need to set up and run a vast number of software or hardware systems on their own. Cloud services can be accessed easily anytime and anywhere, while supplementary storage volume can be added or taken away as needed.

The features and benefits of the cloud support long-term growth.

Google is one technology vendor that has its eyes set on helping the healthcare industry. Its fully managed serverless data warehouse, called BigQuery, enables companies to gain insight into their data without running a hosting infrastructure. The company says it can “process petabytes of data (even in parallel) faster than can be done on premises.”

Organizations such as Cleveland Clinic are using the cloud to make more use of their data. Beth Meese, administrative director of technology and innovations at Cleveland Clinic, said the academic medical center is using the Google Cloud’s Apigee platform to open up its EMRs and run analytics and predictive models. The goal, she said, is to make the data more actionable for clinicians.

While the cloud provides incredible efficiency benefits to healthcare organizations, analytics solutions provide the ability to dissect and present the data intelligently. Creative and detailed visualizations of the data allow stakeholders to absorb the information more quickly, as well as act on it quickly.

Healthcare organizations continue to strive for new and innovative ways of clearly communicating data, often complex, to clinicians and administrators whose valuable time needs to be focused on gaining insight, as opposed to wrangling data.

We recently published a guide that explores how data and technology can enable organizations to make informed healthcare decisions, produce better patient outcomes, and create a better patient and stakeholder experience. You can download it below.

This blog was co-authored by Tom Lennon.

During a five-week IBM training program, I learned a few things about how to sell data science models that I’d like to share it with you. The program was explicitly designed to educate and familiarize IBM’s business partners on how to expand relationships with clients; an introduction to emerging tools; and a glimpse into IBM’s future so that business partners strategically leverage clients in the competitive AI movement.

Drew Conway’s Data Science Venn Diagram

You’ve probably seen the Venn diagram with skills required to be a data scientist such as hacking skills, math & statistics, and substantive expertise. The combination of these skills are what makes a good data scientist. However, you can be a unicorn and possess all the skills and develop a phenomenal model, but the majority of your success will depend on if your model goes to production or project sponsor/ client approves it. The ability to sell your model is as crucial as the aforementioned skills.

In my experience as a data scientist, we are good at developing models, but not so good at selling these models. Sometimes, when we are presenting our models we encounter challenges: code does not run, the information is too technical, not enough material is covered, data is not available (in my experience), you can’t find your latest model, or you got off track in presenting your creation. As Murphy’s law states, anything that can go wrong, will go wrong.

So, I decided to compose a list of points on how to sell/ productionize your model.

Quick Tips for Selling Your Data Science Model

1. Know your audience

Either you are working with a data scientist, a C-level executive, or a manager. You should know who are the stakeholders and design your presentation accordingly. For example, whenever you are presenting to non-technical personnel you do not need to go into coding part of the model. If you are showing a Jupyter notebook, it is a good idea just to go through the output and hide the code, because this will only confuse and create more questions. If you have a data scientist/ technical person in the room, schedule a different meeting to go over the technical aspects of your model.

2. Engage your audience

Engaging your audience will help them stay focused on a subject through the iterative approach of asking questions throughout your presentation. For example, if you are discussing your model try to trigger their limbic system (the part of the brain concerned with instinct and mood) by asking a question about their company, or information related to the model you created.

3. Prepare a story

Storytelling is an essential part of the modeling process. I like to call this process “wow them then how them.” This excellent idea is to show your audience the results of your model and then tell them how you got there. Think of it in a way as a magic trick. First you pull the rabbit out of a hat and then you tell them how you did it. The idea is to keep your audience focused on the process of you going through the model.

4. Prepare for unexpected

Whenever you present your model within the notebook, always have PowerPoint presentation available with images from that notebook. If something doesn’t go according to plan, you will have PowerPoint as a backup.

5. Bigger than you

This tip may not apply to you; however, it is imperative.  Whenever you’re presenting a model make sure you use names that have credibility, because credibility creates a relationship. In your presentation try to use the names of the tools/ platforms that are well-known. This will help you build trust with key individuals.

6. Demand action

Start driving your discussion toward action as you present your model. Drive management to choose the next step.  Don’t assume they will get back to you, because it’s often difficult for management to see the value immediately or they’re busy. So, use your presentation and model to compel them to act.

These are my 6 points of how to sell/ productize your model. As data scientists, we all have different skills and abilities. So this list of points may not work as well for you as it worked for me. Still, I would love to hear your feedback and suggestions on possibilities or methods you encountered with presenting your model. Leave your comments below.