Every day, 2.5 quintillion bytes of new data are created worldwide – giving businesses access to new sources of information that they can use to create better experiences for their customers and confirm for many that big business knows pretty much everything about you.

Last week, I was in Boston for sales training and sampled Jameson Orange for the first–and last–time. The choice of something so sweet is out of character for me and not part of my normal shopping patterns. Ten minutes later, when I got my phone out of my pocket to book my Uber ride back to the hotel, pretty much every internet ad that I saw was for Jameson Orange. Spooky, or some near real-time analytics at work?

To get any value out of their collected data, businesses must build internal data pipelines to perform a series of steps. They must collect the data, validate and potentially enrich it, store it, and make it available in a usable format before they can even think about heavy-duty analytics.

However, in most cases, even these steps are not enough. Because data comes in so many different forms and formats – text, numbers, charts, graphics, video – it can be hard to reconcile it and to present it in a way that tells a story that is easily understood. And because of the rate at which data changes, the value of your data diminishes unless your infrastructure can keep up because it is delivering yesterday’s news.

The tool that pulls all of the data together to tell a detailed and coherent story, reflective of this instance in time, is real-time data visualization.

A little over a century ago, English illustrator Frederick Barnard first voiced the phrase, “A picture paints a thousand words.” Today, the idiom has taken on new life with the rise of powerful new data visualization tools that help business analysts make sense of the chaotic mishmash of information flooding into their data ecosystem.

Data visualization tools are valuable facilitators for human brains that process visuals 60,000 times faster than they do text. They are also valuable productivity tools: visual data discovery tools are significantly more likely to unearth valuable nuggets from the data than managed reports and dashboards.

Data visualization benefits organizations in a number of ways:

• It Uncovers Hidden Insights: Real-time data visualization enables businesses to create outreach plans using up to the second data about customers’ purchasing preferences.
• It Reveals Hidden Connections: Putting the data in a visual format makes it easier to determine how different data points are connected to each other. This helps determine patterns and trends that would be hard to extract from siloed data stores. For example, I recently spoke to our District Attorney about the correlation between crime patterns and phases of the moon; this hunch was validated when the two datasets were presented together, and they saw a consistent upswing in crime in the period surrounding a full moon.
• It Speeds Up Decision-Making: Real time data visualization provides insights that help decision-makers make better decisions faster. Without visualization tools, analysts would spend more time cross-referencing reports, looking for information and responding to requests.
• It Encourages Customization: Visualization tools give analysts the ability to present the same data to different audiences in different ways.
• It Makes Data Exploration More Fun: The ability to categorize, correlate and group data encourages analysts to expand the scope of the datasets that they are working with, leading to richer insights and better and faster decision-making.

In addition, real-time data visualization creates opportunities for companies to generate value they never could have without it.

Real-time insights can also help increase sales. Using real-time analytics, retailers can offer customers contextual suggestions while they are shopping. I have noticed that when I shop for a home improvement project online, the store’s website will make suggestions to ensure that I have everything I need to complete the project. Whereas when I shop in-store, I typically have to do two or three trips to Home Depot before I can complete the project.

For companies that purchase large amounts of commodities for their operations, being able to visualize market trends can make a big difference to their bottom line. They can pick out patterns, buy oil at its cheapest point, or maximize overseas investments based on currency changes.

Companies that need to respond to developing crisis situations can use real-time visualization to mitigate risk. If a storm is coming, a retailer can react on the fly to changes in weather patterns to shift safety products to stores that need them most.

Real-time data visualizations can also help with security and fraud prevention. They enable security officials to reduce day-to-day risk by pulling data from different sources and consolidating insights into graphical forms in one place.

Data volumes are growing at rates that were inconceivable 10 years ago. The variety, velocity, and volume of data that organizations generate make prudent, thoughtful data analysis more challenging every day. Having the right tools to analyze and apply data can completely shift how you make, measure, and scale your business processes across your organization. Request a consultation to make managing your data easier and get the most out of your data management systems. to make managing your data easier and get the most out of your data management systems.

About Emma McGrattan

Emma McGrattan is CTO at Actian, leading global R&D in high-performance analytics, data management, and integration. With over two decades at Actian, Emma holds multiple patents in data technologies and has been instrumental in driving innovation for mission-critical applications. She is a recognized authority, frequently speaking at industry conferences like Strata Data, and she's published technical papers on modern analytics. In her Actian blog posts, Emma tackles performance optimization, hybrid cloud architectures, and advanced analytics strategies. Explore her top articles to unlock data-driven success.

Healthcare organizations now require insights that are only possible to gain by bringing together diverse and disparate clinical, financial, and operational data from across the organization, as well as outside it. Without access to these insights, improvements in outcomes and operational efficiencies that value-based care models promise to deliver will be harder and more costly to achieve. This is where a healthcare data analytics hub comes into play.

What is a Healthcare Data Analytics Hub?

A healthcare data analytics hub provides a unified, cloud-based platform that supports enrichment, analysis, visualization, and reporting services to both automate and act on healthcare delivery, operations, and administration activities. The goal of the hub is to create standardized, normalized data that can easily be computable and leveraged for business intelligence. What distinguishes it from data lakes or traditional data hubs, is that it provides the tools needed to transform data from disparate sources into actionable insights intended for a range of uses and functional groups.

Cloud computing is quickly making inroads in healthcare, with the strongest demand for this software coming from IT professionals in the healthcare space. As a result, providers and payers have started to take note of this demand and are adopting various cloud technologies. Hybrid and multi-cloud adoption is eliciting the most traction from healthcare organizations as they seek a path forward from traditional legacy applications.

Challenges for Healthcare Data

Because of the intricacies and complexities of the profession itself, healthcare companies are faced with a unique set of needs and challenges, including:

Privacy and Security

Issues such as bad actors and ransomware attacks targeting patient data, with HIPAA compliance mandates, privacy and security enforcement are more manageable.

Data Complexity

Patient matching is one aspect of larger master data management challenges in healthcare. Factoring in healthcare’s many standards, code sets, value sets, and local practices and codes, the process for data integration heightens an already complex process.

Capacity for Change and Interoperability

As organizations adopt and utilize new technologies at their own pace, they need better, faster, and easier ways to operate with both new internal and external systems and data sources.

Data Quality

All data integration projects naturally run into data quality issues, which are inconsistent in the way they adhere to various standards.

Skills and Technology

For many health systems, modern tools are unfamiliar as they have historically used legacy tools for every use case. Even if many systems are modern, there is undoubtedly legacy data and systems that must be considered in the grand scheme of value-based care.

To mitigate these issues, a healthcare data analytics hub deals with data as it arrives. The hub mitigates data complexity and security issues by seamlessly linking data across applications, databases and organizations. It resolves data quality issues by producing clean, computable and optimized data for analytics and various other use cases. It takes data that is incompletely coherent to a particular standard and turns it into a computable format that is acceptable and complies with healthcare standards. Data complexity is resolved by aggregating and summarize data in an optimized way, mitigating the skills and technology gap. Overall, a healthcare data analytics hub helps healthcare organization automate and streamline operations and administrative responsibilities.

Benefits of a Healthcare Data Analytics Hub

A healthcare data analytics hub provides a unified platform that helps automate healthcare data delivery, reduces operational overhead, and provides more reliable automation and data sharing. Other important benefits include:

1. Innovation. Be prepared for new and changing care and delivery models while building more efficient, effective, and standardized care pathways.
2. Value. Reduce IT infrastructure, development and integration costs while also investing in high-impact performance improvement programs.
3. Speed. Accelerate developer productivity, improve optimize workflows for clinical and administrative users, and process financial payments in a timely fashion.
4. Usability. Reduce clinical burden, improve the patient experience, reduce friction between payers and providers and improve overall care to the public.

Get More Out of Your Data Platform

A healthcare data analytics hub supports you in managing data across various systems, allowing you to drive change throughout your healthcare enterprise by cataloging, modeling and analyzing data with ease. Your ecosystem of payers, providers, and other professionals can gain greater insights and drive better outcomes with a trusted, flexible solution for managing data.

With Actian’s Healthcare Data Analytics Hub, businesses can connect, manage, and analyze their data to make the most informed, meaningful decisions and drive better outcomes with a trusted, flexible, and built to scale solution.

About Actian Corporation

Actian empowers enterprises to confidently manage and govern data at scale. Actian data intelligence solutions help streamline complex data environments and accelerate the delivery of AI-ready data. Designed to be flexible, Actian solutions integrate seamlessly and perform reliably across on-premises, cloud, and hybrid environments. Learn more about Actian, the data division of HCLSoftware, at actian.com.

Use the Good to Outweigh the Bad

Instead of reacting to negative customer experiences and feedback once damage has already happened, an organization is more successful when it takes a proactive approach. It may seem obvious, but the time and effort it takes to overcome a negative experience is much greater than that of maintaining a positive one.

Consider a study on romantic relationships from the 1970s. Two researchers discovered the difference between happy couples and unhappy couples was the balance between positive and negative interactions during conflict. The study found happy relationships had a ratio of 5:1, meaning happy couples had five or more positive interactions to counter each negative interaction. That same recipe for success translates to other types of relationships too, including relationships with customers.

In the business environment, customers require even more positive interactions for the good to outweigh the bad. For example, when it comes to customer reviews, the positive-to-negative ratio can be as high as 40:1, as explored in this Inc. article. This happens because unhappy customers are far more likely to write a review than happy customers. Add in word-of-mouth, and recency bias – there’s more trouble than meets the eye. Customers have higher expectations of product and service quality now than in previous years, which makes vying for their business even more competitive.

We can also look at this from a Net Promoter Score (NPS) perspective. NPS is a market research metric that asks customers how likely they are to recommend a company, product, or service to a friend. NPS is a valuable way to gather insight into how an organization is currently performing and identifies opportunities for improvement. NPS responses fall into three ranges:

• The detractor range is 0-6, categorized as unhappy customers who are unlikely to repeat customers or refer a friend.
• The passive range is 7-8, which includes customers who are satisfied but not excited enough to promote the company or product.
• The promoter range is 9-10, the most loyal and enthusiastic customers. It takes constant dedicated effort to maintain this high score.

The Negative Impacts of a Poor Customer Experience Strategy

When navigating the customer experience, past performance is not indicative of future performance. We’ve all heard it, and when it comes to your brand, it is 100% true. Customers are constantly evaluating and comparing brands. Brand equity takes years to build but can be destroyed by a single tweet – that’s reflective of the impact of a poor customer experience strategy. It adversely impacts the premium a business can command for its product and services and diminishes customer lifetime value. Brands that score low are less profitable and brands that score high are more profitable. The London School of Economics estimates that a 7% increase in NPS increases revenue by 1%.

That’s not all though. Bad customer care has a ripple effect beyond customer retention and growth; it impacts internal team morale and attrition, increasing stress and deteriorating emotional health among employees. Just as it takes lots of time and effort to overcome a bad customer experience, the same is true for employees. It’s difficult to replace great employees. Happy customers and happy employees go hand in hand.

The Importance of Product Design

The most ever-present way a customer interacts with a company is through the company’s product. It’s one of the forefront factors in how customers judge you. Some argue the product is the brand and experience; others say the experience is the product. I believe the entire customer journey is the experience, and products and services provide valuable decisioning influence.

Product design is important in a customer experience strategy, but it is not a magic bullet that can overcome other poor areas of the experience, particularly bad customer service. Examples include long wait times, no live agent to resolve an issue and having to repeat the same information when transferred to different agents. Before, during and after the product, are experiences that either add to, or detract from the journey. This reminds us to break down functional-based silos and not project those onto customers.

All of this leads us to some key customer experience strategy advice:

• Make the customer experience so enjoyable that people don’t want to leave you. Think long-term.
• Stay grounded, honest, respectful, and open to feedback. Learn and pivot quickly.
• People first. Do what’s best for others.
• At the root of all design and product goals, deeply understand what problem or solution you are trying to solve. How are you making someone’s life easier?
• Once you’ve solved this, look at it from different perspectives.
• Challenge assumptions, and always be mindful of ways to improve.

Further Reading

Check out this resource for more customer experience strategy tips Customer Data Analytics Hub provides details on how to get real-time actionable insights across all your customer experience data silos. There’s also some useful information on a reference architecture to build a unified customer profile. Learn how to educate and empower customers.

About Actian Corporation

Actian empowers enterprises to confidently manage and govern data at scale. Actian data intelligence solutions help streamline complex data environments and accelerate the delivery of AI-ready data. Designed to be flexible, Actian solutions integrate seamlessly and perform reliably across on-premises, cloud, and hybrid environments. Learn more about Actian, the data division of HCLSoftware, at actian.com.

Big data is revolutionizing virtually every industry, perhaps none more than financial services. It is giving finance firms the ability to do things they never could before – like roll out new payment systems, deliver data-driven offers and use AI to combat fraud.

Banks, investment firms, stock traders, and others have more data at their disposal than ever before. To generate positive business outcomes, they must master the art of organizing, accessing, and analyzing this vast amount of structured and unstructured data to pull insights out in efficient, timely, and cost-effective ways.

Legacy data management systems are struggling to keep up with the myriad sources and different types of data flowing in at higher velocities. Data platforms operating in the cloud provide a solution to these issues across industries. They also have the power, storage, and scaling capabilities necessary to solve specific data-related challenges that financial services firms face.

Regulatory Requirements

The finance industry, of course, is one of the most tightly regulated of all industries. Many countries require data to stay in their country, which makes it difficult for firms to pull reports and perform analytics in finance across geographical boundaries. Firms wanting to look at how a payment instrument performs in one country vs. another, or on a global basis, face challenges accessing and analyzing that data. Modern data management tools enable them to set up data warehouses country by country or region by region. Analytical tools can study the data in stages, with queries getting rerun against different warehouses, all using one platform.

Data Quality

Data quality is critical in financial services because firms generate reports and perform predictive intelligence based on the data they have. Because data comes from disparate sources, quality is often suspect. There might be some data missing or in a different format. Data management tools can preview the data that is collected, and integration tools can translate data from one format to another. Data platforms can fix data quality issues within systems and integrate with other data quality management solutions.

Data Governance

Because financial services firms also deal in sensitive data, they must maintain fine-grained control as to who has access to specific reports. This is especially true for personally identifiable information (PII). Plus, organizations must adhere to data governance rules, as certain types of data can only be “kept” for certain time frames. Using database management tools, financial companies can comply with timelines on transaction and processing data and create governance rules on access and archival data.

Data Silos

Data silos are a significant problem for financial services companies. They often have credit data, customer data, and marketing data in separate warehouses, governed by separate sets of rules. Data integration tools can connect the sets in one warehouse, where departments can run analytics across forms, functions, and geographies. Data management tools provide the capability to connect to different sources and generate reports in one format.

Data Security

As hackers intensify their efforts and broaden their intrusion tactics, financial services firms must respond with tougher security strategies. It is a challenge because every piece of data that gets brought in or shared must be authenticated for each database it connects with. Organizations need to encrypt data warehouses to ensure that data is secure. Integration tools and analytics software also play a key role in providing access to secure data warehouses.

Moving Forward With a Data Strategy

Financial services firms are no stranger to data. They have been collecting and analyzing big backlogs of information for decades. But today’s data requirements dwarf those from previous decades. For those looking to adopt a big data strategy or refine their current tactics, a methodical approach makes the most sense.

Here are some steps they should take:

• Interview internal and external stakeholders.
• Evaluate the current state of systems, processes, and skills.
• Identify a problem space to focus on.
• Create a roadmap for transformation.
• Develop a platform for data collection, organization, and analysis.
• Utilize a cloud data management platform that aligns with your strategy to accelerate this step.

Financial services firms recognize the value data can provide. They are developing new and creative ways to pull insights from data to do a better job connecting with customers and driving efficiencies through their own operations. Taking advantage of tools like the Actian Data Platform can provide the strategic advantage they need in today’s competitive environment.

About Vamshi Ramarapu

Vamshi Ramarapu is VP of Actian Data Platform Engineering, leading cloud data management development. He has 20+ years of experience, previously at Mastercard and Visa, focusing on scalability, user experience, and cloud-native development. Vamshi is passionate about FinTech and data engineering, often contributing to research on secure, scalable platforms. His Actian blog contributions explore next-gen cloud data solutions, security, and innovation. Read his articles or insights on building resilient data infrastructures.