20.01.2023 | Nikolay Valiotti
Complete Guide on Data Monetization: How to Make the Most Profit from Your Data
Data is the new oil that can show your weaknesses and spot ways to get rid of them. However, the majority of businesses are still hesitant to unlock its potential and are afraid of change. According to recent findings, half of Sisense respondents admit best data practices are important for future-proofing business performance. At the same time, 15% prefer to maintain the status quo because change is too risky.
Data analysis, when done thoroughly, is a key that opens a door of opportunities, not a malware undermining your achievements. To unlock them, you often need to get rid of data analytics myths. We have analyzed our clients’ prejudices and come up with top misconceptions that prevent businesses from becoming data-driven. To better classify them, we have broken them down into several categories by data maturity.
A common myth is that there’s nothing difficult about collecting data and processing. Anyone with basic experience in data analytics can handle the task. This is often supported by the following prejudices:
All this is supported by the idea that your data analyst is competent enough to do magic with figures, especially if you subscribe to multiple software packages. In reality, you shouldn’t hire someone with questionable experience to design and implement data analytics strategy. Here are some best practices to help foster success:
With data analytics outsourcing, you may fall in the same trap: “Data experts know what they are doing.” Most likely, they know how to deliver analytical insights to improve your marketing campaigns. Yet, it doesn’t mean you shouldn’t dive into details and critically analyze the results. The same applies if you wish to get trusted data foundation and processes to continue leveraging data yourself. Having code and reports won’t help you to figure out how to successfully maintain Big Data architecture and source business insights.
Suppose you found data scientists to spot data points and set up the collection process. At this stage, many tech companies are trapped by one of the biggest data analytics myths—data storage helps to solve any problem. If the challenge remains, just collect more data! In reality, raw data is useless without correct and thought-through data visualization that emphasizes pain points.
Here are some key mistakes and ways to overcome them:
|If data duplicates occur when collected from different sources, we will handle it.||Deduplication should be addressed in advance and data should be unique for each source.|
|Reports can be created from raw data.||Tables should be relational to start building reports.|
|Any random information can deliver valuable insights.||Data quality policies and data management standards are your best friends.|
|Ad-hoc approach to report and dashboard building is okay.||To leverage data science benefits come up with compliance rules.|
|Reports are just tables that show nothing.||To spot bottlenecks make a visualization injection.|
|Data is easy to upload after several tests.||Do your homework in advance if you want to avoid extra data analytics costs.|
|Ready-made solutions and ad interfaces are enough.||Even cutting-edge data products may mislead you as they often show figures to their favor. Invest in custom Big Data analytics infrastructure.|
|Collecting data chaotically (especially if it’s client-related) is a decent approach.||You won’t be able to use data until you abandon Excel and deploy streamlined data collection.|
If you don’t think that forecasting is a lot of astrologers and realize the tangible benefit of data, you will reach a predicting point. At this stage, you no longer rely on executives’ guts only but shape your business path with figures in mind. As a result, better-informed decisions result in better business outcomes. However, suppose you believe that your data scientists can easily forecast any critical metrics or define further business development. In that case, we have bad news for you—it’s another popular data analytics myth.
Those who want to get answers should ask correct and precise questions. Otherwise, you are unlikely to improve your decision-making process and will complain about the wasted cost of data. By the way, you don’t need a hefty investment to become data-driven. Sometimes basic data sets and regular reporting are enough to notice the first changes.
Some tips to improve forecasting:
You are only in the middle of your data analytics journey. Most likely, you already know how to collect, process, and store data and are equipped with some data analytics software. Even all of these aren’t enough to drive informed decisions. At this stage, you may be stuck on whether to proceed with a specific or generic approach.
A single strategy with no segmentation doesn’t make sense. Clusterization by consumption type, RFM, or products allows you to notice the minor specifics that may cost you extra sales.
The same applies to traffic. Enhanced business decisions are possible only when you acknowledge traffic types and analyze them accordingly. It also works for conversions that should be separated in a database (for example, online companies may differentiate desktop and mobile devices, website and chatbots, etc.)
Don’t underestimate the conversion period. There is a big difference for your social media marketing when to launch paid traffic: seasons, holidays, weekends, and even timing matter.
Key Performance Indicators or KPIs are an integral part of data analytics, be it data analytics department management or data analysis, for the sake of improved decision-making. Let’s address both.
Believe it or not, some companies are sure that those dealing with data do not need a KPI. Often, such an approach results in a quick experiment shut-down because top management sees little value.
The trap awaits those who think every department contributes to business prosperity and assess data experts’ work by company revenue. In reality, there are many more factors, except for quality data analytics, that may affect your profit and loss statement.
Here, the myths are more versatile. While some believe their business is particular, so they need special KPIs, others avoid advanced proxy metrics and go for general ones only. There is another group that prefers to go off the beaten path and create its own KPIs. Eventually, they waste tons of time on nothing instead of dealing with critical business questions.
Data analytics is a marathon that requires streamlined processes and due attention to deliver value. Otherwise, you will deal with data excerpts that eventually can’t guide you. To ensure a consistent and effective approach, you need a strong backup: in-house data experts or a data analytics outsourcing agency.
However, when hiring, don’t fall for beautiful wrapping in the form of polished cases. Find out how a data analytics specialist will solve your problem. If you find a professional with strong hard and soft skills, no myths can stop you from making informed decisions. Want to test if you suffer from data analytics prejudices or not? Talk to the Valiotti Analytics team!
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