Trust matters when it comes to relationships. Therefore, it matters when it comes to the customer experience.People line up at the Apple Store’s Genius Bar in part because they’ve come to trust that they will get a great customer experience.Whether you’re a B2B or B2C, how can you build trust with your customers?To find the answers, we connected with Sandeep Kharidhi, General Manager, Data and Analytics Platforms at Deluxe.For a constructive conversation packed with practical advice for building trust and relationships, enjoy this episode.Sandeep Kharidhihttps://www.linkedin.com/in/sandeepkharidhi/The Data Standardhttps://datastandard.io/https://www.linkedin.com/company/the-data-standard/

This episode features an interview with Sireesha Pulipati, Google Cloud Data Specialist at Google. In this episode, Sireesha talks about the agility the cloud provides, how data can help personalize customers’ experiences, and much more.Sireesha Pulipatihttps://www.linkedin.com/in/sireesha-pulipati/The Data Standardhttps://datastandard.io/https://www.linkedin.com/company/the-data-standard/

On this week’s episode of the Data Standard Audio Experience, we talk with Chris Bergh, the CEO and head chef at Data Kitchen. DataKitchen’s mission is to provide the software, service, and knowledge that makes it possible for every data and analytics team to realize their full potential with DataOps.Highlights from this week’s episode include:Chris’ background and how the lessons learned in the Peace Corps and at NASA apply to him todayWhy AI left Chris feeling like a jilted loverMost projects that people do in data analytics failThree things that DataOps focuses onComparing and contrasting DevOps and DataOpsThe types of data that DataKitchen handles and building a product or a service around DataOpsFixing problems at the source instead of just offering a tool to slightly improve things downstreamWhere we are at in the process of how companies are going to run on dataChris Berghhttps://www.linkedin.com/in/chrisbergh/The Data Standardhttps://datastandard.io/https://www.linkedin.com/company/the-data-standard/

Users of large online dating sites are confronted with vast numbers of candidates to browse through and communicate with. To help them in their endeavor and to cope with information overload, recommender systems can be utilized.  Given the massive user bases of these dating sites, there is a need and value in helping users combat information overload by filtering the most relevant partner candidates of the abundant pool of choices. Otherwise, users have a hard time finding a partner, as they have to browse through and communicate with potentially hundreds, if not even thousands, of users and profiles. While important attributes such as age, location, gender, and relationship preference can be used to cut down on the number of viable candidates, the leftover set may still be huge especially for users living in densely populated areas. Recommender systems are a means to combat this information overload and provide users with candidate recommendations personalized to their preference and profile. Moreover, there is value in such recommender systems, even for smaller dating sites, because users’ attention and time can be devoted to only a handful of choices at a time and poor matches may lead to repeated rejections and discouragement. Recommender systems may also be used to reduce the burden of popular users, who may receive unruly amounts of messages, and relieve the anguish of unpopular users and users who continually get rejected by balancing the way users occur in recommendations.Dinh Thi Buihttps://www.linkedin.com/in/dinh-thi-bui/The Data Standardhttps://datastandard.io/https://www.linkedin.com/company/the-data-standard/

In this episode of the Data Standard Audio Experience podcast, we discuss with Netrality Data Centers, the evolution of the edge and how the deployment pace has accelerated. We also dive into the strategies that will support growth in the months and years to come. Craig Waldrop, VP of Channel & Strategic Alliances at Netrality Data Centers, joins The Data Standard to discuss how his industry and company use data to drive their businesses.Craig Waldrophttps://www.linkedin.com/in/craig-waldrop-416586/The Data Standardhttps://datastandard.io/https://www.linkedin.com/company/the-data-standard/

Following a data-driven approach is the best way of running a successful business. Data collection and analysis applies to many different areas- from understanding customer trends to reducing operational costs. But data also applies to risk management. By using analytics to assess business risk, you can develop more secure systems that protect against cyber-attacks.Risk assessment is a critical part of implementing a data security plan. However, many companies assess risk by merely referring to previous experiences or industry trends. This approach is less specific and may result in higher costs, non-compliance with established regulations, and vulnerability to potential data breaches.Luckily, following a data-driven approach will help your business protect customer data and assess the effectiveness of current controls. Risk assessment via data analytics will also result in the implementation of better risk management strategies.Prasanna Madabushihttps://www.linkedin.com/in/pmadabushi/The Data Standardhttps://datastandard.io/https://www.linkedin.com/company/the-data-standard/

Information as an asset is still in the “early adoption” phase, which makes it a competitive differentiator for leading organizations as they focus on digital transformation. In turn, data and analytics become strategic priorities.Data and analytics are the key accelerants of an organization’s digitization and transformation efforts. The phenomenon of Big Data has changed the business world like never before. The most important part of this transformation is the strong emergence of analytics to support the shift in modern enterprises from a process-centric viewpoint to one that is more data-centric and data-driven. The data that surrounds the enterprise is being harnessed into information that informs, supports, and drives decision-making in a timely, repeatable manner.Sudeep Krishnanhttps://www.linkedin.com/in/sudeepkrishnan/The Data Standardhttps://datastandard.io/https://www.linkedin.com/company/the-data-standard/

Advisors constantly juggle between meeting the short-term demands of their businesses while working towards their longer-term goals. To complicate things further, short- and long-term views are frequently not aligned at a particular moment in time—creating a greater lack of clarity and frustration.While it’s important for all advisors to check-in with themselves regularly to assess the balance between the two, it’s even more important for those considering a move. While the “perfect opportunity” would solve for all goals equally, it’s unrealistic to expect that perfection actually exists in one single option.So how do you find the right opportunity when your goals are seemingly at odds? You start by breaking each down, identifying both short- and long-term needs and goals, and ranking them by importance.Chris Murphyhttps://www.linkedin.com/in/chris-w-murphy/The Data Standardhttps://datastandard.io/https://www.linkedin.com/company/the-data-standard/

In this episode, guest Austin Lee sits down with The Data Standard to discuss how he found a rhythm for his work life and personal routine throughout the pandemic.Austin Leehttps://www.linkedin.com/in/austinrlee/The Data Standardhttps://datastandard.io/https://www.linkedin.com/company/the-data-standard/

As the world consumes more and more data, businesses increasingly require experts who know how to handle large amounts of customer information, competitor research and product performance results. They also need professional developers to build and manage their increasingly complex websites and apps. These two distinct needs have led to two exciting tech fields: data engineering and software engineering. The two sometimes overlap, but actually have different areas of expertise.To those outside the tech world, terms like “data engineer” and “software engineer” may sound interchangeable, since both rely heavily on programming skills. Some may even assume that data engineers and software engineers do the same thing.In reality, software engineers usually only engage in data infrastructure to a limited extent. Their primary focus is on building websites that function well for the end-user. The task of data engineers, however, is to maintain large datasets and build the structures that house them. So while each profession involves a common background of data management, software engineers and data engineers have vastly different responsibilities.In short, data engineers examine the practical applications of data collection and help in the process of analysis. Data engineers work closely with large datasets, and build the structures that house that data long-term. Data engineers should be well versed in programming languages like SQL that allow them to build, maintain and secure big data. Most data engineers have been software engineers at some time in their career and have moved on to specializing in data engineering.Software engineers are also skilled in computer programming, but they perform work that includes designing and developing operating systems, as well as front and back-end development. While some software engineers rely heavily on data, you’re not guaranteed to focus on data analysis if you pursue a career in engineering. Engineers who focus on front-end web development, for example, are much more concerned with the look and feel of a website, rather than organizing the data on the back end.Daniel Peckhttps://www.linkedin.com/in/peck/The Data Standardhttps://datastandard.io/https://www.linkedin.com/company/the-data-standard/