Software as a Service

Software as a Service: No Data Scientist Required for Business Benefits

Big Data isn’t just for big businesses. To compete in today’s marketplace, small and mid-size businesses need access to the same tools and resources that the big enterprises are using. But how many organizations can afford to hire a single data scientist and purchase the costly software they need to discover useful insights?  

The United States has over 30 million small and medium-sized businesses. In recent years, we’ve seen these companies grow thanks to the increasing number of Software as a Service companies. The right business intelligence tools can really level the playing field between startups and well-established businesses. Cloud-based software solutions and open source software have removed the barriers that previously prevented smaller companies from leveraging these solutions. Businesses are reaping the benefits of Predictive Analytics while offloading the headaches of implementation and maintenance to Software as a Service (SaaS) providers. 

What’s the Role of a Data Scientist or Data Analyst?

A data scientist—also known as a data analyst—is expected to solve complex business problems and make data-backed recommendations by gathering and analyzing information from massive volumes of data. Data scientists generally thought to be data-savvy business people with a strong foundation in computer science, modeling, analytics and math. In addition to these pillars, they need the following traits to be successful:  

  • Practical business sense. A data scientist must know about all the business tools that are available – including the newest platforms and technology – and how they can be financially beneficial. 
  • Familiarity with SQL. SQL, or Structured Query Language, is an essential database management language that can be used to extract and segregate data sets. A good data scientist will have mastered SQL and other tools used to sort and organize data. 
  • Broad knowledge base. An experienced data scientist will have an extensive and diverse range of knowledge about data warehousing or data lake storage systems, machine learning, and data visualization. 

There are many expectations surrounding data scientists, and it comes as no surprise that they are in high demand. Most large enterprises have large teams of data scientists who work with their purpose-built AI models. However, if your company’s budget doesn’t allow you to hire a data scientist, there are still many ways to take advantage of AI platforms, Predictive Analytics tools and other types of Software as a Service. 

Benefits of Software as a Service (SaaS)

If you wanted to launch a new app, you wouldn’t build your own operating system to support it, would you? That software has already been built. Now, you can use it as the foundation for your own product. 

The same holds true for algorithms. Yes, mathematicians and data scientists are needed to think up complex models that are used to process and analyze data. However, a larger group can then use these algorithms to build ready-to-go software systems that meet stringent approval standards. In a nutshell, this is what Software as a Service is all about. With data as the new currency, companies are gaining a competitive advantage by enabling practically instant data collection and analysis solutions.

When is Low Code Development Useful?

Every business has its own unique processes—processes that a generic software solution cannot solve. And a large number of businesses do not have an IT team on-staff to build a custom software platform. But there is another option: A low-code platform that allows non-technical users to achieve customized solutions. The platform provides graphical user interfaces and drag-and-drop features that require little or no technical knowledge. 

Any modern low-code platform must also include version control, performance testing and change management. Many older SaaS solutions were rather buggy, though the stability of modern low-code software platforms is quite good today.  It’s also critical that the SaaS solution that you choose has a library of pre-built components and add-ons. 

Low-code solutions cannot fix all development needs, but knowing when this option is appropriate is important. Low-code solutions are particularly useful for Software as a Service platforms, such as Sertics. Using a low-code development strategy, companies can create their own data lakes, with access to data visualization and Predictive Analytics tools. With Sertics, users don’t have to write the code to call the necessary data sets from wherever they are stored. Instead, a drag and drop interface quickly sends the user down a path toward data discovery. 

Machine Learning to Ingest, Validate and Cleanse Data

Data scientists spend lots of time cleaning, organizing and validating data. Data preparation is an important component in business analytics since data doesn’t always follow standardized formats. Artificial Intelligence (AI) and machine learning may be leveraged to evaluate various forms of data, the data source and the data’s inherent issues. What’s more, this technology can achieve this in a holistic manner, thereby reducing the time involved in “cleaning” data.

Machine learning speeds up large dataset cleaning and unification by working to remove errors, linking-related data and canonicalization. 

  • Data cleaning normally focuses on removing duplicate data, setting time stamps and fixing rows with missing values.
  • Linking similar or identical data from different sources is an essential step in the data cleaning process.
  • Finally, canonicalization puts the resulting linked data into a unified, standardized format. Businesses can also add metadata tags so that data files can be easily located when the need arises. 

While this might seem simple enough, just think of all the different ways you can write a street name. The different contexts give it a different value. For example, machine learning can differentiate between ‘LAX’ and ‘Hilton LAX.’ One is an airport while the other is a nearby luxury hotel. However, if you wanted to group these two data points based on their shared location, a machine learning platform could do this as well. The data janitor tasks associated with complex data cleanup is another task that’s best left to machine learning.  

The most useful business insights and analytics SaaS platform will include technology and tools that allow users to perform the aforementioned tasks. There are few things worse than having millions of data points that you cannot use because they lack proper tagging and organization. Sertics is a SaaS for small businesses and large enterprises alike. 

Interactive Data Discovery Tools

The purpose of data is to provide insights. Looking at long columns of numbers is not the best way to achieve this objective. Data discovery tools use visual presentations to uncover patterns that are difficult to detect by simply looking at the data. No technical abilities are needed to pull data from the cloud and present it with data visualization. Many of these tools are available as SaaS platforms that allow business users to drill down into their data in a number of different ways.

Asking yourself what story you want to tell will help you choose among the various data visualization types such as heat maps, scatter point plots, histograms and more. With Sertics, there are hundreds of data visualization models available. AI helps to choose the best visualization models with the highest degree of relevance to the data in question. Interactive data visualization tools allow you to tweak variables to see a prediction of the changes that would result. You can move, sort and filter variables until you find the best possible result. Choosing the right data visualization tool is a big decision. It will play a central role in shaping your business strategy.

Software as a Service removes the complexity of business intelligence, Predictive Analytics, and data discovery by automating tasks through the use of AI and machine learning technology. Business users of varying skill levels can access the data and understand it in a way that will help them uncover and implement business insights. To experience the benefits of Software as a Service for your business, schedule a Sertics product demo today.

 

Harshit Gupta has more than eight years of experience as a Senior Developer. He holds a bachelor’s degree in Computer Software Engineering and an MS degree in Computer Science from Arkansas State University. Some of his most notable accomplishments include his dissertation on 3-Tier MapReduce, being named runner-up in the SevenTablets Annual Hack-a-thon, and receiving the “think/WOW” award for his work on Lockton-Dunning Benefits.