How Trustworthy Is Big Data?

Businesses and individual users now employ big data analysis to support decision-making, engineering innovation, and productivity levels. However, the surge in the reliance on big data leads to growing concerns regarding its accuracy and trustworthiness.

Although big data provides unprecedented insights and opportunities across all industries, you should be aware of concerns, such as loss of trust in big data, and address them as well. This article explores the perils of bad big data, reasons for the lack of trust in big data, and strategies that can be adopted to combat it.

Understanding the Problem

Trust has become the currency of business in a world where misleading information is the norm. When organizations spend this currency wisely, they can create a competitive edge in this digital world where change is the only constant — fostering a culture that explains the importance of data from within an organization and externally is critical. Also, the business needs to harness customer data and insights to manage relationships and drive sales.

What Is Big Data?

Big data encompasses massive data sets that include structured data, such as databases and transaction lists, unstructured data, such as texts, social media posts, and videos, and semi-structured data, such as web server logs.

Big data systems are often decentralized and combine numerous distributed systems. For instance, a data lake can be at the center of a system integrated with other components, including relational databases and data warehouses. 

Non-relational data is typically uncooked and unrefined, left in the raw state, until it must be processed and organized for specified analytical functions such as business intelligence (BI). Sometimes, it's preprocessed via data mining and other data preparation tools to be served to the applications that run periodically.

Big data thrives on five core elements: Volume, Velocity, Variety, Veracity, and Value. 

This research paper based on the findings from Professor Raj Chetty's pioneering work on social mobility was published by Opportunity Insights, a Harvard-affiliated economic research group. Besides its social and environmental impact, big data has also revolutionized healthcare by enabling you to draw insights that can help in diagnosis and treatment. This research article talks more on how big data impacts healthcare.

Examples of big data sources include emails and texts, videos, databases, data from IoT sensors, social media posts, and webpages. These examples exhibit high-speed information generation, various forms of information, and numerous methods of data capture while valuing the accuracy of the data gathered.

The following code snippet shows how you can read and process big data programmatically:

string filePath = "bigdata.csv";
ConcurrentBag<DataFrame> processedDataFrames = new ConcurrentBag<DataFrame>();
int batchSize = 100000;
await Task.Run(() =>
{
    Parallel.ForEach(ReadDataInChunks(filePath, batchSize), chunk =>
    {
        DataFrame dataFrame = ProcessData(chunk);
        processedDataFrames.Add(dataFrame);
    });
});

The ReadDataInChunks method reads data from the file in chunks, where the size of the chunk is passed to the method as a parameter. The following code snippet shows the ReadDataInChunks method:

static IEnumerable<List<string[]>> ReadDataInChunks(string filePath, int chunkSize)
{
    using var streamReader = new StreamReader(filePath);
    var header = streamReader.ReadLine();
    var chunk = new List<string[]>();
    while (!streamReader.EndOfStream)
    {
        var text = streamReader.ReadLine();
        chunk.Add(text.Split(','));
        if (chunk.Count >= chunkSize)
        {
            yield return chunk;
            chunk = new List<string[]>();
        }
    }
    if (chunk.Count > 0)
        yield return chunk;
}

Here is the source code of the ProcessData method, which accepts a list of string arrays and returns a DataFrame instance.

static DataFrame ProcessData(List<string[]> data)
{
    var columnA = new List<int>();
    var columnB = new List<double>();
    foreach (var row in data)
    {
        if (row.Length >= 2 &&
            int.TryParse(row[0], out var val1) &&
            double.TryParse(row[1], out var val2))
        {
            columnA.Add(val1);
            columnB.Add(val2);
        }
    }
    return new DataFrame(
        new PrimitiveDataFrameColumn<int>("Column_A", columnA),
        new PrimitiveDataFrameColumn<double>("Column_B", columnB)
    );
}

This program reads raw CSV data in chunks of 100000 rows. We've used Parallel.ForEach method to process the data in a concurrent manner. The processed data is returned as an instance of DataFrame. Note that you should install the Microsoft.Data.Analysis NuGet package in your project and include the Microsoft.Data.Analysis.DataFrame namespace in your program as shown in the code snippet given below:

using System.Collections.Concurrent;
using Microsoft.Data.Analysis;

How Does Big Data Work?

Big data is a technology that can process enormous volumes of data that traditional databases cannot handle effectively. This data can be structured, like the data found in different relational databases, or unstructured, like social media posts or email texts. 

Here is how big data works: The first step is data collection which involves gathering massive quantities of data from multiple information sources including social media platforms along with e-commerce portals as well as online financial transactions and digital communication types.

After the data has been gathered, the next step is to integrate this data into a centralized system for processing. Integration refers to consolidating data from various sources and formats into one storage area, such as an on-premise server or a cloud data lake.

The next step is about managing the data. This entails ensuring that the data is accurate, secure, complete, and available for analysis. Next, this data is analyzed to uncover patterns and correlations in the data, and then determine actionable insights using technologies such as machine learning.

Organizations need to protect critical data against cybersecurity threats and unauthorized accesses. In addition, an organization needs to follow the regulations mandated for data protection and ethically and legally maintain personally identifiable information.

The Problem With Big Data

Big data is often inaccurate and when the data is inaccurate, it can cause big problems to an organization that uses this data for making business predictions and strengthening relationships with their customers. Data consistency, privacy, security, accuracy and completeness are the key problems associated with big data. Let's now understand each of these in more detail.

1. Quality of the Data

Big data systems usually involve raw, unprocessed data. It is on this data that several data reduction, scrubbing, and processing technologies are applied. One of the major constraints in this approach is that there is hardly any control over the quality of the data stored in the data lake. An organization that can deliver high-quality, trusted data can make the most out of big data. 

A major challenge for many enterprises is data quality when they are dealing with enormous amounts of data. You need relevant, timely, accurate, and trustworthy data for successful data analytics.

2. Inaccurate Data Due to Human Involvement

Humans and data quality errors are inseparable, and human error is the biggest threat to your data. Such errors can be due to spelling mistakes, duplicate entries, incorrect inputs or even inconsistent formats. Hence, there is a need to address human errors to ensure that the data is consistent, clean, and reliable. Colin String, the author of the book "Humanizing Big Data," says that we should look at the human side rather than the technical side of things to be able to achieve positive results with big data.

Data processing cannot be automated completely, and human involvement will always be needed. The organizations should rather have strategies in place to eliminate human errors in big data. The organizations should be able to identify the data entry points and all the channels and understand how information enters their systems. 

3. Data Security

In the recent past, some of the biggest data thefts have occurred. As such, security of data is an important consideration around big data. Although storing data in the cloud is economical, there are security concerns for your data. The cloud storage companies have their security measures, but you cannot be sure that your data is secure. So, measures have to be adopted to secure data to combat the worst-case scenarios.

Data security and privacy are major issues for the companies. To combat this, the organizations should invest on the right anti-malware software and adopt the right security measures to ensure that the data stored and presented is secure.

4. Data Completeness, Accuracy, and Validity

Business-critical data should be complete. Data completeness and accuracy are two of the major problems, and this starts from the time the data is collected. The recorded value may be approximate rather than the actual value. Even IoT data may only be approximate. In many instances, data is lost in transmission. Even if the data is accurate, you still need judgment and human intervention to determine if the data is correct and useful. As an example, you cannot bank on social sentiment data as a measure of customer satisfaction.

How Can We Make Big Data Trustworthy?

Organizations often don't have control on the data sources that they are analyzing, i.e., social media feeds and data that comes in from public repositories. So, now that we know the challenges faced by the organizations in accuracy and quality of big data, let’s discuss what can we do about it.

Defining the ROI

It is imperative that organizations ascertain the business value of big data, i.e., identify the areas where the usage of big data and data analytics can add business value, and estimate the impact of poor quality information. Moreover, organizations should be able to estimate the investment needed to ensure trust and proper use of big data.

Clean Data Over Time

Maintaining clean and accurate data is always a challenge, and it needs continuous review. In an era where more and more decisions are based on data, it is imperative that the data be accurate and clean. Additionally, keeping data clean should be a regular practice. This would enable the organizations to review the information and ensure the quality of the data. The organizations can also take advantage of verification processes to evaluate and verify data using software solutions.

Impart Training to Staff

Organizations should offer the necessary training to employees to make them aware of what big data is all about and how to work with it. They should also explain the importance of accurate data to the staff and train them on how information captured is used in a business. Employers should impart training to their employees so that they are aware of how to use information responsibly.

Employees of an organization that leverages big data for business analysis and predictions should be trained on data security. The users should have restricted access to sensitive data, i.e., you should allow only authenticated users to access the data that they might need.

Data Validity

Even when the data is entirely accurate, judgment is required to assess its relevance. You need to be able to make a big data-based decision, even though that can be a very unreliable source of truth. For example, a plunge in how customers talk about a product on social media could portend disappointing financial results a couple of quarters later.

Better Governance and Transparency

A properly planned approach towards robust governance is needed and the business users should be made aware of the appropriateness of big data and limitations of data accuracy. Business users need to know where the big data will come from to be able to make informed decisions. The knowledge of the known limitations, i.e., the kinds of decision-making that the data can and cannot support should be available.

Takeaways

Today's digital economy relies on data to provide numerous services and products. However, without integrity, the trust and value of the data become next to nothing, and the worth generated further decreases.

In the future, digital transformation and business models will rely heavily on big data. In the digital age, big data is a key component of business models for the future. In addition to helping themselves, companies with strong systems and credibility are better positioned to utilize these new technologies. 

Organizations should, however, have effective strategies and processes to guarantee the reliability of data. Only those organizations that have a strategic and proactive framework to get consistent, accurate, and clean data can be successful in providing reliable insights using big data.