It can take a long time to analyze data. For datasets smaller than 100 gigabytes, the process usually takes a few days. For larger datasets, the same process can take weeks.
That's because the conventional process to prepare and analyze data consists of multiple steps, including data collection, cleaning, integration, transformation, analysis, and visualization. With so many steps and multiple tools coming into play on each one, analyzing large datasets can prove to be quite challenging.
Product managers rely on data analytics to uncover valuable insights and make informed decisions. A long and tedious process for analyzing data means significant delays in decision-making. That is why product managers and data professionals use augmented analytics to analyze large datasets.
What is Augmented Analytics?
Augmented analytics is the use of artificial intelligence and machine learning techniques in the analytics process to automate data preparation, generate insights, and help in decision-making.
Thanks to the use of machine learning, augmented analytics can analyze large datasets with greater precision and efficiency.
How Augmented Analytics Work
Product managers rely on augmented analytics to make major decisions about the apps they manage. That's why it is important to understand how augmented analytics work. The process is comprised of three main stages:
1. Data Preparation to Integrate and Clean Data
Augmented analytics tools automatically clean, transform, and unify data from various sources. This eliminates the need for manual data cleansing and ensures that the insights generated are based on accurate and reliable information.
Think of a retail company that receives sales data from its physical stores and online platforms. The data from these sources will be in different formats and will likely have inconsistencies and missing values. To prepare this data for analysis, someone will have to clean the data, convert dates to a uniform format, and fill in missing values. A large retail company with multiple stores cannot rely on a manual or partially automated process to clean data and will have to use data preparation tools to automate the whole process.
Tableau Prep and Microsoft Power Query are good examples of tools that can be used to automate the data preparation process. You can extract, clean, and combine data from a wide range of sources and set up automated workflows that follow your rules and do the process for you.
2. Insights Generation to Identify Patterns During Analysis
Augmented analytics identifies patterns, trends, and anomalies in the data with the help of machine learning algorithms. It highlights significant insights that might have gone unnoticed through traditional analysis methods.
Let's say an ecommerce company gets hundreds of thousands of transactions every week. Every time someone visits their site, browses a number of products, and leaves, they gain valuable data that will help them learn more about user behavior. If the company can train machine learning algorithms to analyze customer purchase history and browsing behavior, they will be able to improve their user experience and offers accordingly.
You can train machine learning models to analyze data however you prefer, but faster and on a much larger scale. Google Cloud AutoML and Tableau are good options to train machine learning models for insight generation.
3. Natural Language Interaction to Ask Questions and Get Answers
One of the remarkable features of augmented analytics is its ability to communicate insights in plain language. For example, a marketing manager can use a business intelligence tool with natural language interaction to ask, "What were the sales trends for our new product last quarter?" The tool would instantly generate visualizations and insights in response, eliminating the need for complex queries.
To achieve this, you need tools that help you query the system using natural language, receiving easy-to-understand explanations and actionable recommendations. Use Microsoft Power BI, Google Data Studio, or Tableau Ask Data for this third stage. Additionally, you can use a combination of tools for all three stages, or you can use Tableau or Microsoft Power BI to handle all three stages from the same platform.
Benefits of Augmented Analytics
Product managers in the mobile app industry can reap numerous benefits from using augmented analytics in their workflow:
- Augmented analytics accelerates the decision-making process by providing real-time insights, enabling product managers to respond promptly to changing user preferences and market trends.
- With augmented analytics, product managers gain deeper insights from their data, enabling them to make strategic decisions based on a comprehensive understanding of user behavior and app performance.
- By automating data preparation and analysis, augmented analytics reduces the manual effort required for traditional analytics tasks. This allows product managers to allocate more time to strategic planning and innovation.
- Augmented analytics identifies user patterns and preferences, helping product managers tailor mobile app experiences to individual users, ultimately enhancing user engagement and satisfaction.
- Through augmented analytics, product managers gain a holistic view of app performance metrics, helping to identify areas that require improvement and optimization.
As datasets become larger and larger and organizations rely more on data to make key decisions, augmented analytics will replace the conventional methods of data analysis.
Key Features to Look for in Augmented Analytics Tools
When evaluating augmented analytics tools, keep an eye out for the following features:
- Look for tools that completely automate the process of data cleansing, transformation, and integration from multiple sources. Ideally, there should be no manual steps involved in the preparation process.
- You should be able to train your own ML models to uncover meaningful insights from complex datasets. Go for tools that let you do that, but if that's not feasible, you can also choose tools that come with their own models.
- Look for natural language querying capabilities in your augmented analytics tools, enabling product managers to interact with data using everyday language.
- The tools you choose should provide interactive and visually appealing dashboards for presenting insights.
Additionally, look for tools that provide APIs and SDKs when using augmented analytics for mobile apps. This allows for easy integration of augmented analytics functionalities into your existing mobile app infrastructure.
Common Challenges Related to Augmented Analytics
While augmented analytics offers remarkable benefits, product managers may encounter some challenges:
Data Quality Concerns
Relying on automated data preparation might raise concerns about data accuracy and reliability. It's important to validate the data sources and maintain data integrity. To address this challenge, implement master data management (MDM) strategies to manage and maintain consistent and accurate master data across various systems and departments. Also use automated data quality tools like BigEye that identify and rectify issues in real-time, reducing the chances of flawed data affecting analytics.
Integrating augmented analytics may require a shift in the organization's analytics culture, necessitating change management efforts. To address this challenge, implement pilot programs with a small group of users to showcase the benefits and success of augmented analytics. Use their positive experiences as evidence for broader adoption, and then gradually introduce augmented analytics instead of a sudden full-scale deployment.
Using augmented analytics tools effectively requires a certain level of data literacy. Providing training and resources can bridge this gap. To upskill your team, offer comprehensive training programs that cover both the basics of augmented analytics tools and more advanced functionalities. Tailor the training to different skill levels and roles within the organization. Start with a high level introduction to augmented analytics for data professionals and product managers. Then, continue to include more advanced courses with time.
Frequently Asked Questions
Is augmented analytics suitable for all types of mobile apps?
Augmented analytics can be applied to a wide range of mobile apps, regardless of their industry or niche. It's especially beneficial for apps that deal with significant user data and require data-driven decisions.
Do I need a dedicated data science team to implement augmented analytics?
While having a data science team can be advantageous, many augmented analytics tools are designed with user-friendliness in mind, allowing product managers with varying levels of technical expertise to benefit from them.
Can augmented analytics tools work with existing analytics solutions?
Yes, augmented analytics tools often complement and enhance your existing analytics solutions. Integration options like APIs and SDKs make this process smoother.
How does augmented analytics ensure data security and privacy?
Augmented analytics tools should adhere to stringent data security and privacy standards. When selecting a tool, ensure it complies with relevant regulations and provides robust security features.