Frequently Asked Questions
Questions
- Can data analytics be automated?
- How can data analytics help businesses?
- What are the different functions of data personnel?
- Are data visualization tools interactive?
- What data visualization software is the best?
- Do you need to code to perform data visualization?
- What is the difference between data analytics and data visualization?
- What is the difference between data science and data visualization?
- What is the easiest way to use data visualization in my organization?
- Can I use data visualization in sales?
- Will automations create job loss within my organization?
- How can automation complement human labor?
- How can data visualization identify trends?
- When should I use data visualization?
- What philosophy does Heroic Hippo use for when to automate something?
- What languages does Heroic Hippo use for development of automation?
Can data analytics be automated?
Yes, data analytics can be automated through a process called machine learning which creates artificial intelligence. Data analytics is most commonly not automated due to the costly nature of data processing and lack of population size. I would recommend performing automation around data visualization and data centralization which are far less costly and have a near-immediate payback on investment.
How can data analytics help businesses?
The purpose of data analytics is to develop information out of data. Information should be the backbone of decision making within businesses due to the increased understanding a proper graphic provides. Performing data analytics not only helps with decision making but it can also install processes to improve efficiency of operations. Data analytics can be a large endeavor for a company to properly implement. Taking the first step in improving your understanding of your organization through data analytics can have large financial gains associated to it.
What are the different functions of data personnel?
All of the following roles are critical to using data properly. Some people may fit into multiple roles but having each role is critical for success.
Data Wrangler
Raw data comes from many different locations and formats where columns with different names may separately identify the the same data. The purpose of data wrangling is to identify and create mapping so that many different sources of raw data may be co-mingled without duplication or misinterpretation for further analysis.
Example: Many sources may identify an address of a customer differently. One source may identify it in 4 columns(address, city, state, zip) while another identifies it in 1 column(Full Address). The process of data wrangling would identify this difference and transform the raw data set of one to match the other when matched with each other.
Data Scientist
This role typically is the most broad within an organization in regards to the technical skills needed. A data scientist takes the data and creates a process to make the data meaningful through modeling. Data visualization is a likely output for a data scientist which requires programming knowledge, statistics, and mathematics to create information and actionable insights from data. Architecture and structuring should be a primary skill that a data scientist possesses.
Developer
A developer in regards to data is someone who understands programming languages and how those languages can create automated results. Some of most popular languages to help with data are SQL, Python, R, and DAX. Many developers will identify as a “full-stack developer” which means that they hold an understanding of many coding languages and can complete and end-to-end solution for a project using many different languages. The role of a developer is to generate the raw data, store the raw data, pass the raw data, and connect the systems which analyze the raw data.
Data Analyst
A data analyst should be a multi-functional role. An analyst should have a great understanding of what the data along with what the business does. A data analyst should also be the communication portion of a task force when seeking additional data from the rest of the team. A successful data analyst is able to provide reports and presentations to management while also seeking further answers for management through the rest of the data team.
Are data visualization tools interactive?
Data visualization software is interactive and has much greater value when purchased as an interactive tool. Depending on the engagement purpose, the output of data visualization can be static or interactive. The purpose of data visualization is to act as a tool for transforming raw data into east-to-understand information. An interactive dashboard provides the most information and allows for gathering additional data not easily accessible through excel. Many companies choose to purchase visualized static reports based on manipulated excel sheets which is a less expensive alternative.
What data visualization software is the best?
Data visualization software is widely available online and can be inexpensively created. The two most used software for data visualization are PowerBI and Tableau. Both software have their advantage, Tableau being owned by Salesforce while PowerBI is a Microsoft Product. Heroic Hippo performs the majority of their work through PowerBI due to the investments Microsoft is taking towards the development of their processing and data portions of their business.
Do you need to code to perform data visualization?
No, data visualization has many components associated with the generation of the visualized reports. Coding is necessary for the transformation of raw data but is not necessary for the interpretation through charts and graphs. A strong understanding of excel graphics provides a large advantage to someone learning how to turn data into dashboards through data visualization.
What is the difference between data analytics and data visualization?
Data analytics is the performance of a process to identify data, transform, and process the data into information. A common output data analytics is through data visualization, a report or a dashboard. Data analytics is a broad term where data visualization is a component of data analytics. Data visualization is a key component towards interpretation and making available additional information not typically available easily through other output formats.
What is the difference between data science and data visualization?
Data science and data visualization work hand-in-hand to provide meaningful information to organizations with large amounts of data. Data science is the process of coding that provides mathematical interpretation of the data. A data scientist focuses on turning the data into information through what is available in the data and the goals presented. Data visualization is the process of displaying information into a graph, chart, dashboard, or report and is a key component towards proper interpretation of the information available.
What is the easiest way to use data visualization in my organization?
There are many easy ways to start using data visualization in your company. Pivot Charts within Excel are the most used form of data visualization at the moment but not necessarily the easiest. I would recommend downloading the free version of PowerBI and loading in a sales and customers dataset as there is usually a lot of valuable inferences which can be created that are not easily identifiable while also being easy to use.
Can I use data visualization in sales?
Data Visualization is most under-utilized within sales. Much of the value most technologies present through PowerPoint would be better explained and approached through Excel. I previously sold Marketing Technology with data visualization. The impression of our company was much more valuable based on us using a real demo dashboard rather than relying on pictures to walk someone through our technology.
Will automations create job loss within my organization?
While we do not control the path an organization takes in employment, automation should be used as a way to increase value-add activity into the production of products and services to customers. My favorite use of automation is increasing the production capacity of an organization through replacement of low-value monotonous tasks with high-value quality control and value-added labor hours.
How can automation complement human labor?
Monotonous tasks create low-wage labor and provide little advancement opportunities for people that hold these jobs. Automation provides for the ability to reduce monotonous tasks while promoting skilled labor and increasing the ability for a company to pay higher wages through increased efficiency by automation. This higher efficiency complements the labor force in the organization by making them more productive and often having higher morale while working due to higher pay and more meaning within their work.
How can data visualization identify trends?
Data visualization is the process of displaying information into a graph, chart, dashboard, or report and is a key component towards proper interpretation of the information available. Though trends and correlations can be calculated without visualization, visualizations provide a much more efficient way to identify what is needed to be calculated and explored. It is also easier to communicate trends to other people or in presentations when the information has visualized into the proper graph.
When should I use data visualization?
- Reports are generated on a daily, weekly, or monthly basis for management
- Looking to expand territory to new market or looking for a new location to build
- Customer retention is trending downward
- Prospects have many structural questions in sales presentations
What philosophy does Heroic Hippo use for when to automate something?
Data should be informational and should lead a company towards decision making. There should be a cost analysis when choosing to automate a process where a payback period is calculated. Automation should not be used where human interaction can prevent large unnecessary issues. A quality control team who understands expected automation outputs should be implemented to ensure results meet standards held by the company. Evaluation of automation should be performed on a yearly basis to identify cost savings and additional opportunities to enhance current automation with new technology.
What languages does Heroic Hippo use for development of automation?
Heroic Hippo has the capabilities to run full-stack development to develop automation and visualization for furthering an organizations efficiency and understanding. The primary languages we use are related to analytics and efficient languages for processing of data. Those languages are Python, R, DAX, and JavaScript.