Big Data vs Bad Data - How to Cut Through The Noise and Improve Your Growth

Post by 
Adam Crow
Published 
May 5, 2021

Poor data quality hinders business growth, it's that simple. 

Gartner’s 2017 Data Quality Market Survey reveals how poor data quality causes organisations to take a financial hit each year  - an average of $15 million in losses. Another 2013 study by Experian titled State of Data Quality further shows how poor data quality causes an average company to lose 12% of its revenue. 

In Australia, many companies are unable to leverage their data to achieve business objectives due to incomplete data and lack of trust in data. The Global Data Management report found that Australian businesses considered at least 26% of their customer information to be inaccurate. In fact, 65% of companies in Australia are finding it hard to turn their customer data into useful actionable insights. 

Bad data does not only breed organisational mistrust but also creates far-reaching consequences leading to customer dissatisfaction, increased operational costs and unnecessary lost revenue. A few inaccurate records may seem insignificant at first. But as you continue to scale, these issues can compound ultimately harming your business’ bottom line. 

More specifically, it can cause:

  • The failure of your Big Data projects translating to wasted resources and major digital transformation setbacks;
  • Misinformed or under-informed business decisions caused by skewed results and a lack of insight;
  • Operational inefficiencies driving increased costs and employee dissatisfaction; 
  • Poor customer experience (CX) outcomes impacting future purchase behaviours and your brand’s reputation; and
  • Lost revenue and opportunities damaging your competitiveness in your industry.

But how does bad data usually happen? Working with various clients from different industries over the years, we’ve seen six common causes of data quality problems.

How Bad Data Happens

Data entry errors

Inaccurate or missing data is usually caused by incorrect manual entries. This includes typos, data entered in the wrong field or variations in spelling and formatting. 

Failed data migration 

Changing systems and migrating your data to new platforms can also cause problems with data quality. As every transfer runs the risk of getting the data mixed or misplaced. 

Lack of data integrations 

Data integrations combine data from multiple sources giving users a single unified view of their data. They also help assure the consistency and quality of your data through its entire lifecycle. Without it, there is a greater possibility for inconsistent definitions, formats, rules and values to occur.

Poor quality of data source

Data sources with statistical biases, outdated information, falsification issues or even software bugs have the potential to negatively impact the veracity of your data. Poor quality data sources also tend to produce duplicated data wherein the exact copy of a record is created in the same or another database.

Data silos

Data silos fragment information causing conflicting data between departments. And because it isolates data, it can cause data decay rendering the once relevant information to be inaccurate and outdated. 

Compliance issues

Companies that refuse to keep up with the changes in data privacy laws are unable to leverage the benefits of 1st party data. Instead of having data collected directly from their audiences - data that is high-quality and highly relevant - they settle for third party data that may be poorly aggregated and lack accuracy. 

The value you get from having a variety of information arriving in increasing volumes and at a faster rate greatly depends on having data that is free from errors, inconsistencies and redundancies. While avoiding poor data quality seems to be an ongoing challenge for almost all businesses, there are steps you can take to ensure Big Data constantly delivers faster and better insights. 

We’ve listed below some of the best practices you can use to cut through the noise and improve your growth.  

Making Big Data Work

Understand what Big Data really means

Big Data has three defining properties that differentiates it from traditional data - volume, velocity and variety. 

  • Volume - Big Data is about volume. It arrives in large amounts through various sources such as your social media platforms, websites, customer data platforms (CDPs) and online applications.
  • Velocity - Refers to the speed at which data streams into your organisation and is being processed, filed and retrieved to meet the demands of your business.
  • Variety - Refers to all types of data formats available ( i.e. structured, unstructured and semistructured) enabling you to get more insights for better assessment of opportunities and problems. 

Additional dimensions of Big Data have also emerged over the years which speaks to the importance of the data’s value and truthfulness. 

  • Value - What value do you get from Big Data and how can you translate it into tangible benefits? Will it help you improve your services and reduce costs?
  • Veracity - The degree of accuracy and reliability of your data sets. In the context of Big Data, aside from looking at the quality of the data itself we also need to ensure the trustworthiness of its source and processing.
Develop a Big Data strategy

Your Big Data strategy is your vision for how you want to collect, manage and apply your data. It sets the foundation for every action you take related to your data and defines the role your data will play in achieving your business goals. 

When setting up your Big Data strategy, ensure that it aligns with and supports your organisation’s vision, mission, goals and strategy. Work with the other business stakeholders in your organisation to fully understand their needs and verify the functionality of your Big Data project. 

Assess existing platforms and processes

Review where you currently stand when it comes to your data analytics capabilities. What are the applications and platforms where your data is collected and maintained? Do you have a 360-degree view of all your data or is there an issue of data visibility? 

Getting clarity on your existing platforms and processes will allow you to identify the gaps in resources, potential hurdles and the opportunities this type of digital transformation will help you deliver. 

Identify data sources and assess their quality

Understand the data you have (i.e. the total amount of data you have collected stored) and still need. Further, identify your data sources and conduct an assessment of accountability and quality for each. This will help you determine:

  • If your existing data sources are fit for purpose or not;
  • Who is responsible for data quality monitoring; and
  • Where data needs to be enriched. 
Ensure effective data management and integration

Effective data management and integration is a critical piece in every data project. When implemented successfully, it enables you to maximise the value of your data throughout its lifecycle - eliminating data redundancy, enabling data sharing and ensuring data integrity and safety. 

Invest in solutions that allow you to: 

  • Prioritise data security; 
  • Keep your data clean and reliable;
  • Break down data silos and create a centralised data system; and
  • Facilitate data accessibility. 

Big Data Use Cases

Big Data offers a variety of business benefits when it’s strategically aligned with your organisations’ vision. Here are just four of the many ways Big Data can improve business outcomes. 

Product development and optimisation

Big Data plays an important role in the advancement of product development across different industries. Because Big Data comes from a variety of sources, companies can gain a deeper understanding of their customer’s behaviours and ascertain market acceptance. Providing R&D teams with the insights they need to develop products that meet or even exceed customer expectations. 

By harvesting and analysing big data, data-driven companies are also able to reduce product development costs and lead time while utilising their resources more creatively and ensuring product efficiency.

For instance, multinational consumer goods company Procter & Gamble (P&G) uses Big Data to design new products. By leveraging predictive analytics and simulation models, they’re able to create thousands of iterations in seconds to find the best design and composition to ensure optimal product performance. 

Improving customer experience

Big Data is also helping companies establish deeper connections with their customers, improving customer experiences and satisfaction. Instead of relying on instincts, business leaders now use customer data to guide their CX initiatives.

From basic information such as names, email addresses to purchase history and style preferences, the massive amounts of customer data are helping companies to:

  • Better understand customer sentiment;
  • Create personalised offers, messaging and recommendations tailored to where the individual customer is in their journey; and
  • Improve the accessibility and responsiveness of their customer service.

Take for example Burberry, the iconic luxury brand that is using Big Data to boost customer satisfaction. Burberry uses customer data to deliver relevant product recommendations to its customers. Furthermore, a customer’s interaction with the brand and purchase history online is also made available to Burberry’s in-store staff. So that when a customer visits the store, they can provide a more personalised experience and suggest items that match the customer’s preferences.

Driving innovation

The wealth of data flowing into an organisation is becoming a core asset for business innovations. Whether it’s transforming supply chains or developing highly disruptive new business models, companies are exploiting the data they have to continuously create value in different aspects of their operations.

Coles, for example, has started utilising Big Data to help inform the decision making between suppliers and the company. The shared data of more than 1 billion customer transactions from over 2,500 Coles stores will provide suppliers as well as the category management teams the insights they need to forecast demand, and optimise product range and performance. 

Improving risk management 

In today’s digital world, Big Data has drastically changed how many organisations approach their risk management. The almost unlimited access to customer information and user behaviours help leaders identify emerging trends and other factors to prevent fraud, assess operational and financial risks, reduce customer churn rate and lower their employee attrition. 

Starbucks is one of the many companies using Big Data for risk management. They plan their store openings through a data-driven approach. By using various data points like area demographics and customer behaviour, the coffee-house chain is able to better assess a certain location’s propensity for revenue growth. Making more accurate estimates of success rates and reducing financial risks of a new business.


Improve Your Growth with Big Data

According to a BARC research, companies that were able to quantify their gains from analysing Big Data experienced on average a revenue increase of 8%, and a 10% reduction in overall costs. Netflix in particular is already saving $1 billion a year from customer retention thanks to Big Data. 

When managed and applied correctly, data or Big Data in particular, can create significant growth opportunities for your organisation. It will give you new ways to analyse information and gather insights that support more strategic business decisions. However, the value we derive from it will rely on how we handle Big Data and ensure that it works for us and not the other way around. 
At Blended Digital, we support our clients in addressing data management issues impacting their business. We help in recognising, augmenting and surfacing the right information at the right time to ensure positive CX outcomes. Want to learn more about how you can make Big Data work for you? Talk to our marketing technology experts today.

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