The Most Costly Big Data Mistakes You Should Avoid
Marketers are now using data-driven analysis and approaches to utilize accurate research data for their marketing campaigns. Not every data and its digging strategy is relevant and accurate enough to be adopted by a company. The question is how to avoid unauthentic data driving techniques and data blunders.
“Data is the new oil for IT industry”
What does big data comprise?
IMG Source: Researchgate.net
- Volume
- Velocity
- Variety
- Veracity
These four V interprets data quality in terms of its quantity, certainty, and categories.
What is the data-driven marketplace and what importance does it hold?
Researching and collecting data for the targeted customers and audience can help in providing accurate services and information. The brand can better understand what its customers’ demands and expectations are. This helps in driving better leads, conversion leads, and successful campaigns.
“Without big data analytics, companies are blind and deaf, wandering out onto the web like deer on a freeway.” – By Geoffrey Moore, an American Management Consultant and Author
How can big data mistakes damage the company’s marketing profile?
Wrong data-driven methods can result in a poor response from the target audience with reduced engagements. The marketing team will predict erroneous insights that will damage the prospects of marketing campaigns. A survey was conducted in 2013 for big data evolution, where 81% of the companies mentioned big data strategy as their top five priorities and 55% of them reported failure and implementing big data objectives.
Here’s a golden guide on how to identify and avoid significant data mistakes to keep your marketing game running on fleek.
Ignoring quality-driven data
Researching and accumulating data is not the only requirement as quality overshadows quantity when it comes to customer service. Making the data qualitative in terms of its relevance, confidentiality, and accuracy. The data should be sorted, organized, and cross-checked in all the protocols of data quality control.
“Consumer data will be the biggest differentiator in the next two to three years. Whoever unlocks the reams of data and uses it strategically will win.” – By Angela Ahrendts
The fallacious data analytics can develop ineffective insights for marketing campaigns, damaging the perspective and motivation. Infiltrated data can drain the marketing budget into waste by not fulfilling the objective. Take the following precautions:
- Follow the taxonomy governance
- Avoid using meta tags
- Focus on versions
- Scan data on regular basis to find potential threats
It’s vital for businesses to plan their marketing budget based on quality and accurate data. Otherwise, they risk missing out on opportunities to grow and expand.
Arranging your database into sub-datasets
The concept of organizing data in a single database in making marketing decisions can help in removing inconsistency. A large data set can average out errors. Small datasets can escalate the chances of inconsistencies in marketing campaigns and can ruin marketing decisions.
To get more accurate insights and to boost the effectiveness of a marketing campaign, the data analyst must focus on building data as relevant and precise as possible.
Source: Ingrammicro.com
From government to energy sectors, every sector is investing to build its big data market. The financial sector has reported owning $6.4B of market value in 2015.
Data with no marketing goal
Researching and analyzing data with no intention is baseless and a waste of money and effort. Data digging has always to be done under some criteria to meet the requirements of target customers. With no specific analysis intention about data, it would definitely result in ambiguity, inaccuracy, and irrelevant insights. The marketing and execution department of the organization focuses on analyzing from a 360 perspective about framing a committed strategy with its all execution phases.
It’s important to analyze metrics to save the budget and efforts of data analysts. In parallel, it’s vital to track the performance of those metrics to see their worth and effect on the marketing campaigns.
The sample data statistics highlight what significance this one-minute data collection shows. The analysis shows 2.5 million Facebook posts were made in 1 minute, showing massive engagement. 72 hours of YouTube videos were uploaded.
How to surmount this?
Before gearing up the analyzing operation, jot down the goals related to data selection and marketing benchmarks. Through this technique, you can use all the resources to find the optimum data set.
Weak or no data architecture plan
Quality, confidentiality, quantity, relevancy, and accuracy make up a solid data set. A database with no framework and plan is like a balloon full of data but no rope tied with it. A structure-less data set can result in discrepancies and ambiguities in performing analysis actions. It will become a menace to store, retrieve, and save the data.
“Data is a precious thing and will last longer than the systems themselves.” – By Tim Berners-Lee, Inventor of the World Wide Web
Enterprises these days have employees and contractors doing data entry work from home because the scale of these entries is too vast. This unorganized data can suffer from constraints and potential threats. The solution lies in building a Data Architecture Plan with a smooth concrete of storage and retrieving. Data-driven enterprises must automate the process of turning signal intelligence into a decision or action, and the way to do this is by creating automated processes powered by AIOps using Robotic Data Automation(RDA). There are many online and offline storage tools such as cloud and edge computing, coupled with low-latency tools.
Improper data visualization
Presentation and proper showcasing of information and raw data come under the art of data visualization. Apart from organizing and storing your database, it’s equally important to make it presentable and visible. It comes under the skills and responsibilities of marketers and data analysts.
A database that fails to deliver the required information has lost the effort and sense of data presentation. Your Data Architecture plan also comprises data allocation and presentation which allows easy conveying of information.
The data should be organized in sections to make it understandable for the audience. The caliber and requirements of the audience should be considered while designing the presentation of data. Haphazardly organized data can result in visual displeasure. You can use software design tools to creatively save and arrange your data with the help of infographics and visuals.
A team lacking the major analytics skills
Most of the data science and mining companies compromise on improving their teams’ skills and expertise according to the upgrade in technology. There are predictive maintenance tools like those that sensors collect to accumulate massive databases in a single space. All the data analysts must be called for regular analysis training to update them with essential tools for developing accurate data insights. Even if you’ve employed freelancers who were looking for typing jobs from home, you need to arm them with appropriate skills. Most companies and marketers confuse big data by measuring their technical strength of handling it such as storage and computing devices. Whereas, they should be focusing on effective big data initiatives. Once they decide on business strategies for big data, they can allocate supportive technology with it.
“No great marketing decisions have ever been made on qualitative data.” – By John Sculley, CEO of Apple Inc
Zero collaboration between data analysis and business development team
Once your company’s data is set to be cooked for marketing, finance, and business strategies, a lack of a business development sector will bring in no value in the data. A proactive BI team will invest in building its data in terms of its thoughtful resources. They explore and work on driving importance from the collected data for the organization. A Bi team is dedicated to working for the managing, execution, acquisition, and quality-driven utilization of data. Big data requires different forms of treatment of data isolation, management, and authentication which requires some operational procedures.
Comprehend your data set trends
Data collection, analysis, and response show a significant trend for data analysts to understand and work accordingly. When tracking and analyzing the data set, there are various connections in the data that connect different parameters. You can see them as trends or see the interlinks between them.
These trends are not always trustworthy but can help marketers to some extent in some phenomena. The best way to find misleading trends is to look from where the trend comes in. The cause of the trend will signify the credibility of the specific trend.
The bottom line
Massive data analysis and research is a significant sub-department of data marketing strategy. Considering its significance in mind, it has to be error-free as it has a major contribution in developing marketing campaigns and customer-related approaches. To design a flawless data mining procedure for your company, focus on its quality, architecture, and database. As mentioned above, say NO to small data sets as it invites massive inconsistencies.
We can’t argue more on how data has impacted our lives as digital data storage has overpassed 40 zettabytes by the end of 2020. With 70% of the population with their own mobile phone, each individual is contributing some useful data stats to the company, brand, or government. It’s wise enough to say in 2021 that your data commands and control. No one and none of the organizations can control the flow and effect of data. With thousands of copies of a single piece of data all over the world, it’s impossible to control privacy and copyrights.