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While people often get confused between data and information, the two are quite different. Data is in a raw and unorganized form that has to be processed – either by a human or machine – to make it meaningful. It usually includes facts, observations, perceptions, numbers, characters, symbols, and images. Data can be something simple and apparently random and useless until it is properly organized.
On the other hand, information is tangible insights or facts derived from your data. Unlike data, information is processed, structured, or presented in a given context to make it useful. For example, every student’s test score is a piece of data. Whereas, the average score of an entire class is information that you can deduce from the given data.
Another concept related to data and information is knowledge. It refers to your capability to understand what is happening around the data and information. Knowledge is your expertise and wisdom to infer the results from the data and information you have obtained.
So, why are we talking about data vs. information vs. knowledge?
That’s because, as a pay-per-click (PPC) expert, you should know what data is and how to leverage it to gain useful information, which leads to better knowledge that guides your campaign decisions.
There are several ways you can have data in PPC advertising.
Suppose you have a PPC campaign with 20 Ad Groups, and each Ad Group has an average of 20 keywords, targeting four devices with 50 locations for 24 hours a day and seven days a week.
This means your campaign can involve more than a hundred thousand combinations of data.
If you analyze each metric individually, you’ll see there are many complications in the combinations that can leave you baffled. You can check different combinations in your PPC account and get actionable insights to optimize your campaign by using PPC Signal.
In this article, we’ll discuss how to understand data and make it meaningful. Lastly, we’ll look at how you can manage or calculate such complex combinations in your PPC campaigns.
DIKW is a model used to understand the relationships between data, information, knowledge, and wisdom. It looks at several ways of extracting insights and value from all kinds of data: big data, small data, smart data, fast data, and slow data -it doesn’t matter.
The DIKW model is often depicted as a hierarchical model in the shape of a pyramid. Each step up the pyramid answers questions about the initial data and adds value to it. When you reach the top of the pyramid, it means you have transformed the knowledge and insights into a learning experience that guides your actions.
A quick way to climb the different stages of the pyramid (from data to information to knowledge and finally to wisdom) is to use semantic technologies and semantic graph databases. These technologies can establish links between dissimilar and heterogeneous data and extract new information and knowledge out of existing facts.
Equipped with this new knowledge, you can reach the peak of wisdom and acquire a competitive advantage by fueling your business decisions with data-driven analytics.
The DIKW model has its own set of limitations. It is quite linear and expresses a logical sequence of steps with information being a contextualized ‘progression’ of data as it becomes more meaningful. However, the reality is often quite different. For example, knowledge is much more than just a subsequent phase of information.
The DIKW model is still used in various forms to understand how value and meaning are extracted from data and information. However, this model is often criticized for its hierarchical shape, as it overlooks numerous critical aspects of knowledge. In this age of big data, APIs, and the interplay of structured data and unstructured data, the DIKW model is argued to bypass essential steps involved in effectively capturing data and transforming it into actionable insights.
Mapping of the DIKW model to different kinds of information management systems includes:
Owing to the massive potential that big data has to offer, many companies are eager to jump in feet first, gathering as much data as possible. But, when it comes to analyzing the data, quality is far more significant than quantity. Instead of concentrating on how much data you can gather, it’s better to focus on the strategy to collect the right kinds of data and maintain high quality and updated datasets.
One of the main reasons to have a data-driven PPC campaign is to improve your conversion rate and boost the return on investment (ROI) of your marketing strategy. But poorly managed data strategies can have the opposite impact.
If the data you’re analyzing is wrong or obsolete, then the insights you get from that data will be ambiguous.
As a result, you will create a PPC strategy based on incorrect insights, which can decrease conversions instead of increasing them.
So, how can you prevent such a situation?
That’s only possible if you can ensure that your data is of high quality. You need to implement a strategy that emphasizes data quality instead of quantity.
First, you should know what types of data you’re seeking to capture the highest quality of data. Several factors can help you decide whether data is of high quality.
As easy as it may sound, extracting, cleansing, and consolidating data is a complicated process. But you don’t have to worry. There are plenty of marketing automation tools available on the market that provide you with a single, cloud-based platform that is well-suited for data integration. You can use these tools to form a targeted and tailored marketing strategy that will help you execute a strategic plan for gathering data.
Here are four reasons why it’s undoubtedly better to concentrate on data quality instead of data quantity.
Did you know, nearly thirty percent of the data professionals spend 90% of their time cleansing raw data for analytics? That’s a big challenge for data experts. Managing such vast amounts of surplus data frustrates the data specialists and considerably widens the “time-to-insights” window.
As a result, you experience a direct negative influence on your business performance. Instead of dedicating your valuable time to cleaning up data, you need to focus on data collection. Consider taking the time to analyze what elements are important to derive insights, and then adjust your systems accordingly.
Working with a large volume of data is expensive. Why? Because you need an extensive infrastructure to store data. Plus, maintenance and migration of data involve costly equipment and processes.
The higher the volume, the greater the cost. Although there are many cost-effective cloud storage tools available today, there’s no use of hoarding data. Even if you’re spending less money storing junk, your money is still being wasted, where it could have better spent elsewhere.
When you properly extract data, you get clean, reliable insights. However, when you blindly amass data, extracting anything and everything, your data quality becomes poor, resulting in dubious info for decision-making.
As a data-driven business, you have to cope with a large data volume, both internal and external. Plus, there’s a contrast pressure of meeting deadlines. That’s why it is challenging to deploy scalable, impactful solutions to data integration.
When you have to integrate huge volumes of data that include extraneous and needless info, the process becomes sluggish, less manageable, and troublesome. This negatively impacts the effectiveness of your data integration project and further increases your costs as well.
Your data may still not translate into actionable insights due to the following reasons:
With companies producing terabytes of data every day, it’s tough to filter out the noise from the useful info. Moreover, there’s a lack of training, tools, or help needed to manage such huge data volumes.
Marketers find it challenging to guarantee tailored and consistent experiences for consumers as departments are following different parameters and analyzing things differently. Several corporate silos can result in data inconsistency and replication, along with gaps and flaws.
Customers today are more inquisitive about competitors, prices, and several other factors. This behavior is further complicating the customer journey. You can no longer rely on a simple funnel that can be controlled easily. Now it’s like a puzzle where you have to take into account numerous points of influence.
Manually analyzing your campaign data by using spreadsheets may lead to wrong data analysis. Having complex campaign level reporting is essential for success in PPC. You can’t just rely on simple combinations in campaigns, there are a plethora of combinations that are not easily determined using simple tools, so campaign goals, its performance should fit according to the wider picture of business progress otherwise misinterpreted data can deduce wrong results.
As marketing and other business processes have evolved, your analytics should also evolve to keep pace with the changing trends. This is to ensure that your overall strategy and goals remain the same.
Marketing metrics like social media engagements, cost-per-click (CPC), and conversions should become part of the broader conversation about market share, customer acquisition cost (CAC), customer lifetime value (CLV), and more.
As a business, you need to start by clearly understanding the data you already have. You can leverage present data sources, align them with anticipated business outcomes, and apply the insights in a scalable manner. This journey has three primary stages:
Keep in mind that the journey through these stages is usually not a one-time process. Instead, it can be a continuous feedback loop. You will continuously feed insights back into the process to effectively become more knowledgeable with every cycle.
Before you start your journey toward digital transformation, you must keep track of all the data you already possess. Consider organizing the data such that it becomes actionable.
Here’s what you should do:
Take inventory of the data present within your business to reveal previously unknown information sources. This can help you explore, categorize, and leverage your data. However, not all data is created equal. So you should focus on the data that is relevant to sorting out your business problems.
Just because a data lake can handle huge data volumes doesn’t mean it should. That’s because storing excessive data can conceal more critical signals within the data. Using a set of interactive tools, such as PPC Signal, you can explore, evaluate, and transform the collected data for downstream usage.
For instance, as mentioned earlier, a PPC campaign may have thousands of combinations, but not all combinations will prove useful for solving your business challenges. There are only a few combinations that will be of practical use, but it’s not easy to identify them when you have such a large number of combinations.
When you find and bring together all the relevant data hidden within your company, you have to clean, format, and process it to make it usable.
When data is isolated, it is useless. If you wish to use your assets, you should organize, structure, and analyze all your data while keeping the predetermined business objectives.
Consider:
With such a large number of sources and types, your data should have a context to be properly understood, searched, and analyzed. These data sources often look like a goldmine insight. However, if you can’t make sense of the data, they won’t be of any use.
You need to gather, organize, and prioritize these data sources. Although bringing all available data together is an important initial step, it is not enough. Big data and data lakes are inherently unstructured by design.
Unqualified data may lead to insufficient insights. That’s why it is essential to get reliable data. Plus, you should be able to establish data integrity checks for data validity. Preliminary data integrity checks can yield trusted outcomes and quicker return on investment (ROI).
Your business use-case requirements can help organize, process, and analyze requirements for different kinds of data. You can identify the particular technology required and ways to bring them all together in a data management plan.
Another potential advantage of such use-case-focused planning is that it can pinpoint where gaps are present in your data portfolio. When you know where gaps exist in the data, you can understand where your company might need to invest in new assets. This way, you can make more strategic investments.
When you prioritize and validate data, the next step is to extract insights and take action. However, working with complex data sets is not easy. Your human employees who have to take action on findings can’t easily consume rows upon rows. That’s where data visualizations can help. By making your insights compelling, you can deliver data in practical, actionable ways.
The journey begins when you create connections between data. Similarly, you need to create the same connections between operational technologies to end the journey at a new value. As you get control over the data you already possess and use it to reach your business goals, you should start understanding where gaps are present in your knowledge or where new abilities may be needed. This will help your business make smart investments in new assets or systems. Plus, you’ll make more targeted strategic acquisitions as you advance further.
Based on the gap you found on the data, now its time to focus on areas where you can take smart actions to bring improvements in your campaign. In the case of PPC, after analyzing the insights, you can check if the budget is being used properly, are you going fine on wasted spend? Which locations are needing more attention and which devices are performing well. Most important is the search term data that finally triggers your keywords and what conversion rates are going and what improvement is needed there are the crux of the actionable insights.
In our data vs. information vs. knowledge guide, we walked you through the different steps to help save your PPC campaigns from falling flat. You may be wondering how you will adopt all these strategies when there are already so many aspects to think about in your PPC campaigns.
Well, PPC Signal is the answer to this problem, as it’s a powerful tool that uses statistical modeling and machine learning algorithms to provide different scenarios of even the most complex PPC data.
With the insights this tool provides, you can turn data into information, and then improve your knowledge over time through daily practice. This approach to constantly monitor, test, and tweak your PPC campaigns is a crucial practice, which will help you succeed, eventually going from 1X to 10X ROI.
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