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Last updated on June 20, 2023

Organizations can accomplish more with their data than ever before thanks to advances in analytical data processing and data democratization initiatives led by the spread of visualization tools, low-code and no-code solutions, and innovations like data mesh.

Advances in compute power, innovative data processing methods, and broader cloud adoption have accelerated these trends, placing data at the forefront of business decision-making. 

Let’s take a look at how recent technological innovations and new processes are changing the future of data analytics and analytical processing systems.

What is analytical data?

Analytical data is a term that denotes a time-based and consolidated view of an organization’s transactional or operational data. Contrary to transactional processing which focuses on recording events, analytical data processing is all about analyzing data.

Analytical data offers an overview of key business facts to help teams gain insights into past business performance and make informed, data-driven decisions about the future. As you may expect, the former is powered by descriptive and diagnostic analytics and the latter uses predictive and prescriptive analytics.

Companies use analytical data to create reports and dashboards, which promise to empower non-technical users with data-driven insights across the entire organization that drive data-driven decision making. The data democratization trend shows no signs of stopping and feeds into the approaches large organizations take to handle the growing siloization of their data.

How does an analytical processing system work?

Analytical processing usually relies on systems that store massive volumes of business metrics or historical data, mainly object storage. Analysts can carry out analytics on a snapshot of this data at a specific point in time. This data is best managed with a data version control system (git for data if you wish).

But how does an analytics engine get the data ready for analysis? How do organizations transform operational data into analytical data?

It all starts with data pipelines developed by data engineering teams. Today, you’re typically looking at ETL pipelines that extract data from operational systems, transform it inline with business requirements, and load it into a data warehouse or data lake. Once there, the data is ready for analysis. The pipelines are usually managed in an orchestration system.

Analytical databases: a primer

Analytical databases (also called analytic databases) are designed with high performance  in mind. The goal here is to provide teams with rapid query response times and advanced analysis of data. Analytical database software can analyze massive volumes of data quickly, up to 1000x faster than a traditional operational database for demanding analytical workloads. 

To this end, analytical databases are more scalable than traditional databases. They often take the form of columnar databases that can quickly write and read data to and from hard disk storage. This capability is what allows them to slash the response time.

The hallmarks of analytical databases are column-based storage, in-memory loading of compressed data, the separation of storage and compute, and the ability to search data across various characteristics.

Analytical databases are made of various types of data, depending on the use case. For example, a database focusing on market data will include historical price and volume data for financial markets. Organizations may use it to evaluate trading techniques. 

Analytical databases can also include:

  • Transactional data (often used for better customer targeting and marketing)
  • Sensor data (predictive maintenance use cases)
  • Natural language data (used in sentiment analysis for social media)
  • Process data (its analysis helps to identify bottlenecks in processes or understand logistics better)

Advantages of analytical databases

  • Columnar data storage – Contrary to row-based databases, a column-based database architecture enables ultrafast processing of massive numbers of data points within a column. 
  • Efficient data compression – The columnar design opens the door to a more efficient version of data compression, which maximizes the database’s capacity and speed.
  • Distributed workloads – In an analytical database, data is stored on a set of machines called nodes. Users can execute queries across the board since data is stored across separate and parallel servers. This enables highly efficient processing of massive amounts of data.
  • Other advantages of an analytical database include scalability on a horizontal scale, compatibility, and advanced mathematical and statistical capabilities.

Analytical data vs. operational data

A company needs both operational and analytical data systems because they serve different ends and deliver varying insights.

Operational data systems store and process operational data, which is data generated by the organization’s day-to-day activities. Think customer, inventory, and purchase data. High-volume, low-latency access provided by Online Transactional Processing systems is a must-have for teams to get their hands on up-to-date data about literally anything.

Analytical data systems, on the other hand, are slightly more complicated because they rely on data that went through a successful ETL pipeline. Instead of merely capturing data about business operations, analytical data is used by teams to make business decisions like segmenting customers or noting variations in purchase volume. 

Collecting analytical data: best practices and methods

What is data collection all about? It’s the process of acquiring data for use in commercial decision-making, strategic planning, research, and other objectives. Data collection is a key component of data analytics applications and research projects – it’s about finding the data required to answer questions, examine corporate performance, or forecast future trends.

Today, data collection occurs at different levels of the organization.

IT systems collect data about customers, staff, sales, and other elements of corporate operations. Companies also carry out surveys and monitor social media to gather client feedback. 

Data scientists, analysts, and business users then collect data to examine from internal systems and, if necessary, external data sources. This is the initial phase of data preparation, which is all about obtaining data and preparing data for use in business intelligence (BI) and analytics applications.

Data collection may often become a more specialized procedure, especially for researchers in science, healthcare, higher education, and other professions, where specific kinds of data need to be gathered. 

Data collecting methods

Organizations usually collect data from one or more sources as needed to deliver the desired insights. An e-commerce store, for example, may gather consumer data through transaction records, website visits, mobile applications, its loyalty program, and an online survey to monitor sales and the success of its marketing activities. 

The methods used to gather data differ depending on the application:

  • Sensors that collect operational data from industrial equipment, vehicles, and machinery
  • Automatic data-collecting services are often embedded into commercial applications, websites, and mobile apps
  • Data gathering from information service providers and other external data sources
  • Gathering data from social media and other consumer online channels

Data collection challenges

Dealing with large volumes of data

Big data settings often contain vast amounts of organized, unstructured, and semi-structured data. This complicates the early data collection and processing steps. Data practitioners often need to filter raw datasets stored in a data lake for specialized analytics applications.

Choosing which data to collect

This is a critical point for both raw data collection and data collection for analytics applications. Collecting data you don’t need only adds time, cost, and complexity to the entire process. However, throwing out essential data might impact the usefulness of a data set and influence analytics outcomes.

Data quality problems

Errors, inconsistencies, and other difficulties are common with raw data. In an ideal world, data-collecting procedures would be intended to eliminate or reduce such issues. However, in most circumstances, it just doesn’t work this way. As a result, the collected data typically requires profiling to discover flaws and data cleansing to resolve them.

Obtaining important data

With so many data systems to traverse, acquiring data for analysis may be a difficult undertaking for data scientists and other users inside a company. The application of data curation strategies aids in the discovery and accessibility of data. This might entail, for example, developing a data catalog and searchable indexes.

Processing analytical data: key techniques

The data analysis approach you take on will depend on the subject at hand, the type of data you have, and the amount of data acquired. 

Here’s an overview of techniques used for data analysis:

Mathematical and statistical data analysis (selection)

Dispersion analysis – this is the analysis of dispersion in the region across which data collection is disseminated, allowing analysts to determine the variability of the variables under consideration.

Descriptive analysis – this approach looks at historical data and describes performance based on a predetermined standard. It also takes into account historical trends and how they may impact future performance.

Factor analysis – This approach assists teams in determining whether a link between a group of variables exists. It exposes additional factors or variables that characterize the patterns in the original variables’ relationships.

Discriminant analysis – this is a data mining classification approach. Based on variable measurements, it determines what distinguishes two groups from one another, which helps in the identification of new objects.

Regression analysis – it models the connection between a dependent variable and one or more independent variables. A regression model can be linear, multiple, logistic, ridge, non-linear, life data, or any combination of the above.

Time series analysis – in this type of analysis, measurements are spread out over time, resulting in a collection of structured data known as a time series.

Visualization and graph-based techniques (selection)

Bar and column charts – they’re both used to display numerical differences across categories. 

Pie chart – used to depict the percentage of several classes, appropriate for one data set. 

Line chart – it shows the change in data over a continuous time frame. 

Scatter plot – it depicts the distribution of variables in points over a rectangular coordinate system. The correlation between variables can be seen through the distribution of data points.

Gantt chart – it depicts the actual schedule and progress of an activity in relation to the requirements, often used to compare various quantized charts using a radar chart. It indicates which variables in the data have greater and which have lower values. 

Funnel chart – this type of diagram depicts the percentage of each step and the size of each module, so it’s handy for comparing ranks.

Rectangular tree diagram – it illustrates hierarchical relationships at the same level as a tree diagram. It makes good use of space and indicates the percentage of each rectangular region.

Flow map – this type of diagram depicts the relationship between an inflow and an outflow area. 

Artificial Intelligence and Machine Learning techniques (selection)

Artificial neural networks (ANN) – a system that mimics the processing of information in the brain, altering its structure in response to information flowing through it. ANNs are good for dealing with noisy data and are considered reliable in business classification and forecasting applications. 

Decision trees – a tree-shaped model that represents a classification or regression model. It splits a dataset into smaller subgroups while also forming a linked decision tree.

Evolutionary programming – making use of evolutionary algorithms, this approach integrates many forms of data analysis. It’s a domain-independent approach and can help you explore a large search area and efficiently handle attribute interaction.

Fuzzy logic – a probability-based data processing approach that helps teams handle uncertainties in data mining processes. 

Ensuring data security and privacy for analytical data

Model deployment and maintenance are critical parts of any data analysis project since they make data and models accessible and usable to your target audience. 

But how do you guarantee the security and privacy of your data and models, particularly in the context of cloud computing, distributed systems, and open-source platforms? Here are a few methods teams use to achieve a level of security matching common requirements.

Data encryption 

Encrypting your data and models before storing or transmitting them is an effective way to safeguard them. Encryption refers to the process of converting your data and models into a format that only authorized parties with the decryption key can read. This is how you can protect your data and models against illegal access, alteration, or leakage, as well as malicious assaults or unintentional errors. 

You can choose from several encryption algorithms, such as symmetric, asymmetric, and homomorphic, that differ in complexity, performance, and applicability for various applications. Your choice will depend on the features of your data and model, such as size, sensitivity, and frequency of usage.

Given that system stakeholders will require continual access to the stored data, you need to create an encrypted communications channel. When using secure communication technologies like Transport Layer Security (TLS) or a Virtual Private Network (VPN), the content of the connection cannot be understood if intercepted by an unauthorized third party.

Data anonymization

Anonymizing your data and models before sharing or publishing them is another option to preserve their privacy. It’s all about deleting or altering any sensitive information in your data and models. 

Anonymization helps to meet data protection standards like GDPR or HIPAA, as well as ethical considerations like permission and confidentiality. 

However, anonymization isn’t a bulletproof solution because techniques like re-identification and inference attacks might possibly disclose the original information from anonymized data and models. As a result, make sure to implement anonymization strategies such as k-anonymity, l-diversity, or differential privacy, and evaluate their efficacy and limits for your particular use case.

Data governance

A data governance framework specifies and enforces the policies, roles, and responsibilities for managing your data and models throughout their lifespan. It’s a must-have for building the security of your data. 

Thanks to data governance, teams achieve data quality, consistency, and integrity, in addition to data security, privacy, and compliance. Data governance also helps teams to accurately monitor and audit their data and models, and detect any problems or events that may develop. 

Model security

Here’s another thing to consider: secure your models in addition to your data, especially if they’re exposed as web services or APIs that may be accessed by external users or apps. 

Model security entails preventing unauthorized or harmful usage of your models, such as tampering, stealing, or exploiting. What best practices do teams use for model security? A good strategy includes encryption, authentication, model validation, logging and monitoring, and model testing. 

Consider the trade-offs between model security and performance, since certain security measures may have an impact on the speed, accuracy, or scalability of your models.

Wrap up

For many years, business analysts and executives had to rely on in-house data practitioners to extract insights from data. Today, we’re entering the age of data democratization with services and tools that allow non-technical audiences to interact with data this way.

Visual approaches to data analysis are a big topic. Current business intelligence solutions offer methods for visual exploration and accessibility via dashboards.

No-code tools expand access to data analysis by removing any coding expertise that was previously necessary, allowing users to interact with data without having to get in touch with the data team. This not frees up data scientists to engage in more demanding tasks, but it also fosters data-driven decisions throughout the firm because everyone is now capable of engaging with data. 

Then there are technologies like data mesh that aim to decentralize the essential components into distributed data products that may be owned independently by cross-functional teams. By allowing teams to retain and evaluate their own data, it’s no longer the sole property of a single team, but a business component to which everyone contributes value.

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