Big-Data analytics is the new oil. We know that data alone does not make much sense or carry a lot of meaning unless it passes through the analytics funnel. Let’s look at what Data Analytics is and how you can use it to create an edge over your competition.
Sure, Data Analytics is the talk of the town, but what is data analytics, why is it so important, and what is all this hype for?
What is Data Analytics?
Analyzing data sets from raw data to make informed decisions is known as data analytics. It is usually done with the help of specialized tools.
Data analytics techniques and technologies enable businesses to reach data-driven decisions and take an informed course of action. Scientists and researchers also use analytics tools to establish scientific models to verify or disprove theories and hypotheses.
Data analytics techniques provide insights to various businesses, big or small, and industries. From optimization of specialized processes to improvement in the organization’s overall efficiency, data analytics in business is helpful. It highlights metrics and trends that would otherwise be lost in the ocean of information.
A data analytics example would be, consider you are a marketer running an online ad campaign to promote a new solar panel system for residential purposes in winter. You begin targeting the audience who have previously shown intent or bought smaller systems already. As your campaign builds up, you use data analytics techniques and learn through the data generated when people click on the ad.
By analyzing the data about these users’ interests, you observe that many of them are interested in using the solar system for electric geysers and heaters. Probably that’s due to the seasonal need. The new system has better storage capacity. You could tweak your ad to target users who bought the previous systems and like the new features using this information. You could also find new audiences of people who didn’t buy earlier but are interested.
Here are some more data analytics examples from different industries:
- Gaming companies use it to set reward schedules for players that keep most players active in the game.
- Manufacturing companies often record the runtime, downtime, and work queue for various machines and then analyze the data to better plan the workloads to operate closer to peak capacity.
- Content companies use many data analytics to keep you clicking, watching, or re-organizing content to get another view or another click.
So now that we have learned what data analytics is, another term that accompanies it is mainly called data science.
To learn more about how Ascend can help you grow your company and succeed in the big data age, contact us today!
What’s the difference between data analytics and data science?
Big data has become one of the biggest technology components today. Thanks to all the big data analytics, we can gain insights from the data that results in business decisions that facilitate scaling.
We already know the amount of data that surrounds us – we have petabytes and petabytes of data that big companies like Facebook, Google, Twitter are using. So, this should bring us to the question of what data science is?
Data science is a huge field that focuses on finding actionable insights from many raw, unstructured and structured data. You might be wondering what the goal and aim of a Data Scientist are? The main goal is to ask questions, locate wherever they can make use of the data, where the data can be used to answer specific questions.
Data Scientists are concerned with all the data that moves into their domain from the data engineers who structure all the data. These Data Scientists analyze these data sets, performing analysis and eventually applying Artificial Intelligence and Machine Learning to drive future trends and so much more.
Subsequently, this is passed onto the Data Analytics team. So this brings us to what data analytics is?
Data Engineers drive the data into the company. Data scientists work to convert the data into information, which is used by data analysts who go and process] this data, who perform a lot of statistical analysis to bring valuable insights and recommendations.
Data Analysts also work on coming up with visualizations of the numbers and trends. Converting these numbers into very good numbers and driving meaningful graphs is the job of a data analyst. The Data Analyst establishes the best way to fix the problem statement.
Now we have the basics out of the way. It’s time to explain how Data Analytics and Data Science differ in finer detail.
Data Science vs. Data Analytics: A Tabular Comparison
While many people use the terms interchangeably, data science and data analytics are unique fields, with many major differences in their scope.
Data Science
Data Analytics
- Data science is an umbrella term for a group of fields used to mine large datasets.
- Data Analytics can be considered part of the larger data science process
- Data science is not concerned with answering specific queries concerning data.
- Data Analytics works better when it’s focused, having questions that need answers based on existing data.
- Data Science produces broader insights that concentrate on which questions need to be asked.
- Data Analytics emphasizes discovering and answering questions being asked at that specific moment.
- Data science is concerned with the Macro Scope.
- Data Analytics is concerned with the MicroScope.
- The goal of data science is to ask the right questions.
- The goal of Data Analytics is to find actionable insights.
- Major fields in Data Science covers Machine Learning, AI, Search Engine Engineering, and Corporate Learnings
- Major fields in Data Analytics covers healthcare, gaming, travel - any industry with immediate data needs
To learn more about how Ascend can help you grow your company and succeed in the big data age, contact us today!
Types of Data Analytics
Data analytics comprises four basic types.
Let’s take a closer look at the categories.
- Descriptive Analytics
Surface-level analytics provides insight into past performance, trends, and patterns emerging from the data. Descriptive analytics can easily be understood and does not require extensive analytical expertise. It merely describes and determines the “what,” rather than establishing a root-cause analysis or providing a reason for inevitable fluctuations. For example, whether the sales have increased? Or has the number of impressions been boosted?
Furthermore, there are two main techniques widely used in descriptive analytics. Firstly, the data is gathered and presented in a summarized manner known as data aggregation. Later, the researcher reads the patterns to infer their meaning, called data mining. By developing metrics like return on investment (ROI) and key performance indicators (KPIs), descriptive analytics can help measure and track progress.
- Diagnostic Analytics
This type of analytics digs deeper into the data. It brings out a reason for “why” something happened. Diagnostic analytics make the findings from descriptive analysis more meaningful, supplementing it with reason and logic. It helps investigate and explain the fluctuations in data from descriptive analysis. Techniques like root cause analysis, regression analysis, probability theory, and time-series data analytics help analysts develop relationships and dependency in the data, which helps them identify the cause of certain anomalies.
For example, the analyst wants to determine why the sales have decreased in the past quarter? And he also observes that there has been a high turnover of sales staff. Hence looking at the patterns and after running some analytical techniques, he develops a relationship and diagnoses a high turnover of sales staff as the cause of decreased sales.
- Predictive Analytics
As the name suggests, predictive analytics determines future outcomes using the available historical data. Predictive analytics helps foresee the results through the emerging data patterns to take calculated measures and averse any risks. It can also identify the probability of recurring trends. While it cannot provide sure-shot answers, to an extent, it saves a business from making uninformed decisions and falling into pits that could have been easily avoided.
A variety of statistical techniques, such as logistic regression, decision trees, and neural networks, along with machine learning algorithms, can be used to forecast all sorts of outcomes. Predictive analytics can help a business forecast sales, revenue, or success of products and services; hence, it can make the right decisions ahead of time.
- Prescriptive Analytics
After extracting the outcomes and applying forecasting techniques, prescriptive analytics allow for data-driven decisions. It suggests the plan of action and the next moves. It enables the business to estimate the likelihood of specific outcomes to reach informed decisions. For instance, if it is predicted that sales would go up by 40% in the next quarter, prescriptive analytics would suggest hiring more sales staff to fill in for the season.
Prescriptive analytics can find trends in massive data sets through machine learning strategies and techniques. It is more of a complex analytical technique that requires the involvement of computational algorithms and modeling procedures. But despite all the complicated efforts, prescriptive analytics influences the decision-making process.
Usually, the data type determines the analysis and techniques required to measure and conclude the results.
To learn more about how Ascend can help you grow your company and succeed in the big data age, contact us today!
Why is Data Analytics Important?
So far, we have established quite some understanding of data analytics and its types. But you might be feeling overwhelmed with the thoughts of big data everywhere, analysis techniques, and how it operates in real-time. Truthfully, we are all surrounded by data, and using it to make informed decisions can provide businesses with an edge in the industry.
Data analytics enable businesses to respond quickly to emerging market trends and gain a competitive edge over their rivals. Analytics can help organizations boost customer service efforts, predict sales and revenue, optimize marketing campaigns and improve operational efficiency. Eventually, data analytics aims to uplift businesses by making calculated and data-driven decisions. Data analytics can also be used for the following:
- Devise risk management plans
- Identify and create new revenue streams
- Establish opportunities for innovation
- Forecast sales and purchasing behaviors
- Evaluate the impact of marketing and advertising campaigns
- More accurate targeting and personalization to optimize marketing efforts
- Generate and strengthen relationships with quality leads
- Build supply chain efficiency
- Enhance customer engagement on social media
- Identify and eliminate blockers or hiccups within a specific process
- Boost customer acquisition and retention
Though this is not a comprehensive list, data analytics provide valuable insights to establish good practices.
To learn more about how Ascend can help you grow your company and succeed in the big data age, contact us today!
Data Analytics Case Study #1: Bill & Melinda Gates Foundation backed Polio Eradication Project (ERASE)
Ascend Analytics has been engaged since Sep 2021 with Bill and Melinda Gates Foundation for a polio eradication project called Erase. As part of the project scope, the social sector initiative is being run across Punjab, Sindh, and KPK.
Given that the project has an enormous geographical scope and the number of individuals engaged in this project was in the hundreds, monitoring the individual vaccinators was a big task. The system was required to track the time spent by vaccinators in the assigned areas while they were being tracked via GPS, and the reports were sent back to the line managers and supervisors.
The senior stakeholders of the Erase project wanted visibility and electronic record-keeping of their data while generating activity reports of campaigns. To this effect, one dashboard was designed to provide precise visualization. Data analytics tools such as Python libraries for data structuring, SQL for querying the data, and Power BI were used for visualization.
Through Ascend Analytics’s solution to Erase, we managed to simplify execution, tracking, and analysis of the key deliverables for the Bill and Melinda Funded project.
To learn more about how Ascend can help you grow your company and succeed in the big data age, contact us today!
Data Analytics Case Study #2: Incorporate one of the case studies from Ascend Analytics portfolio.
Another instance is where the client requested a comprehensive platform for quick commerce enablement & engagement. Blink Technologies is a quick commerce platform that facilitates restaurants, supermarkets, and retail businesses by providing branded apps and websites to sell online.
Blink aimed to leverage SMEs and local businesses by offering a statistical tool that visualized simplified analytical dashboards.
For instance, tracking the user activity on mobile applications helped understand their navigation behavior, and the business could refine the user interface accordingly. Subsequently, customer retention strategies can be planned to reduce churn out. Also, the business could see the popular products and initiate promo codes to enhance sales. Later, the performance of that specific promo code was also measured via the dashboards and analytical reports. Monitoring and examining the order placement and delivery time helped the business improve the overall supply chain performance.
To learn more about how Ascend can help you grow your company and succeed in the big data age, contact us today!
What tools and techniques do data analysts use?
Analyzing data is a powerful skill that helps you make better decisions. Here are some of the quality tools that can aid analysts.
- Microsoft Excel
Microsoft Excel is one of the top tools for data analysis, and the built-in pivot tables are arguably the most popular analytic tool. A successful Excel spreadsheet provides a digestible snapshot of data. It organizes raw data into a readable format that makes it easier to extract actionable insights. Excel allows you to customize fields and functions that make calculations for you with more complex data.
- Tableau
Tableau is an end-to-end data analytics platform that allows you to prep, analyze, collaborate, and share your significant data insights. It excels in self-service visual analysis, allowing people to ask new questions of governed big data and easily share those insights across the organization. Tableau can handle numerous rows of data like a piece of cake. You can create different types of visualization without disturbing the performance of the dashboards. Additionally, Tableau can go ‘life’ and connect with different data sources like SQL.
- Python
Python is a popular programming language that quickly creates and manages data structures. Its built-in analytics make it a perfect tool for processing complex data. Its algorithms can easily penetrate patterns and correlate information in extensive sets to provide better insights and other critical matrices in evaluating performance. Scientific computing and data analysis is an applied branch of Python.
- R
R is a programming language often used in statistical analysis and data mining. It is a free, open-source software used for data science, statistics, and visualization projects. Analytics use it to identify patterns and build practical models. Data scientists use R for deep statistical examination, supported by just a few lines of code and eye-catching data visualizations. You might use R for customer behavior analysis or product research as a business.
- SAS
SAS is an acronym for statistical analytics software. As an analytical tool, the primary purpose of SAS is to retrieve, report and analyze statistical data. It is used increasingly in Data Science, Machine Learning, and Business Intelligence applications. SAS is a group of computer programs that work cohesively to store data values, retrieve them, modify them, compute simple and complex statistical analyses, and create reports. Whether you’re a data scientist, business analyst, statistician, or forecaster, SAS helps you explore, analyze and visualize data.
- SQL
SQL is the most commonly used data analysis tool by analysts and scientists. SQL performs efficient data filtering and transformation, so we only retrieve the needed data. It is more practical to retrieve all the data and then apply it to operations. SQL is a language that helps data analysts interact with data stored in relational databases. The vital ingredient of a relational database is a table where data is stored in tabular form.
- RapidMiner
RapidMiner is a comprehensive data science platform that fully automates and augments the data prep, model creation, model operations processes. Its Auto Model creates models in just a few clicks using automated machine learning. It is a powerful tool that enables everything from data mining to model deployment. RapidMiner Studio also provides predictive analysis by blending structured with unstructured information and leverages all the data.
- Power BI
Microsoft’s business analytics product, Power BI, is a data analytics powerhouse. It delivers interactive data visualization business intelligence capabilities that allow users to see and share data and insights throughout their organization. Power BI can help us generate quick analytics and insights and help on a large scale by providing real-time analytics using significant amounts of data. It brings the predictive power of advanced analytics, allowing users to create models from their data, enabling organizations to make data-based decisions across all aspects of their business.