Big Data, Cloud & DevOps

Big Data has been the talk of the technological and business world for a while now. While we already see many businesses leveraging from it and its impact on processes, we decided to bring forward an expert opinion on Big Data. Here’s our conversation with Dr. Rich Huebner on the same. 

Business units need to work together, and companies need to know their consumers. By integrating data across teams, enterprises make it easier to analyze consumer information and complete marketing tasks. Big data and data-driven marketing is the new status quo. Marketers need the reports generated by big data to find new business, optimize campaigns and help companies make a profit, and each business has its own needs and its own way of making the most of big data technology. Big data technology, cross-functional integration and social media adoption are powerful and effective ways to reduce costs, discover new opportunities and launch new services and products.

If you have a single data scientist and you already think they should be delivering more to your bottom line than they are news flash: “They suck” and you hired the wrong caliber individual for the job. You may still be able to keep them if they are good, but you need to bring in a type-E rockstar to cement your data arm and redirect the unstoppable ship. A type-E individual doesn’t settle anywhere. If you ask an individual where do you see yourself in 5 years and they respond “Not working here” you have found a real winner.

Data Modeling refers to the practice of documenting software and business system design. A Data model is used to document, define, organize, and show how the data structures within a given database, architecture, application, or platform are connected, stored, accessed, and processed within the given system and between other systems.  Data modeling is required to manage data as a resource, integrate existing Information systems, design databases and repositories, and understanding the business. Using proper modeling and reporting, you can spot business trends, spending patterns, and make predictions that will help your business navigate challenges and opportunities.

If you’re interested in a career in tech, you have a lot of options. Following your interests is a great place to start when choosing a career path, but it also makes sense to look for jobs that have strong growth potential. Some of these jobs might require that you gain additional skills, or even go back to school for a new degree, but the effort can really pay off. Here are 5 of the tech careers that are expected to boom in 2019—could one of them be the perfect fit for you?

SQL is a critical skill. What is SQL anyway? SQL is a query language for talking to structured databases. Pretty much all databases that have tables and rows will accept SQL based queries. SQL has many flavors but the fundamentals stay the same. Professionals and amateurs alike use SQL to find, create, update and delete information from their sources of record. Understanding SQL queries can be fundamental to your work. Professionals and amateurs alike use SQL to find, create, update and delete information from their sources of record.

The most common questions asked is how do I become a data scientist?  It is a fair question for those that are deciding to pivot that direction because they want to eliminate the learning waste that traditional educations are full of. The number one reason I think you will never be a data scientist is your lack of passion.  Honestly, passion fixes everything. The biggest flaw in most people attempting to break into data science is their lack of breadth. People who are passionate have great breadth.

With Big Data increasing and the time-consuming task of data preparation, the industry is looking for better ways to improve efficiency and speed up the change of data into meaningful information. With that said, when analyzing the data journey and the constituents data serves, metadata is the common recurring theme that enables an organization that replaces “data wrangling” with data discovery. Metadata management must be a core competency, a place of innovation and of strategic importance.  Data regulations and sensitive data will need to be managed, and it all starts at a metadata level in order leverage, yet protect those it intends to serve. 

Everybody loves DevOps. That’s because DevOps promises to satisfy the deepest longings of digital business—including fast execution on innovative ideas, competitively differentiated customer experiences, and significantly improved operational efficiencies. But who does DevOps love? It’s a fundamental challenge for anyone leading a DevOps initiative. What passions and motivations are driving your DevOps teams? How do you know? And if those motivations aren’t the right ones, how do you re-direct them? Metrics, it turns out, may hold the answers.

Hard work has always been an important competency for aspiring students to become data scientists. Despite having studied there was still a noticeable gap between what they had studied and what industry wanted. You can be a great data scientist, but you can’t if you stay in a silo. So going to Meet-ups, reading Kaggle forums, reading recommended data science books, following technical thought leaders, can help ensure you are at least heading in the right direction. Finding an industry mentor can also be very helpful. Lastly, fall in love with it. Passion for the topic and intrinsic motivation will help you stand out from the school of fish in the market.