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Organizations regularly rely on big data to make decisions, run the business and strategize for the future. They’ve come to accommodate an ever-increasing array of data sources—both internal and external—and a growing array of tools for consuming the data.
Modern companies use big data every day to understand, drive and evolve all aspects of business goals. But stakeholders need to understand how and why the quality of data is directly linked to the quality of decision-making. Big data, by definition, refers to massive amounts of information being collected at high speed. Failure to analyze it objectively can result in analysis paralysis. However, the same data, if carefully analyzed, can help companies uncover the right insights.
The starting point for this analysis is understanding the needs and challenges of customer buyers, which in turn helps to successfully strategize and understand performance over the course of the business. To scale business, leaders must understand the nuances of finding and collecting relevant data, deriving the most valuable insights and turning them into action.
Of course, pattern recognition is key. It should funnel from multiple sources and merge into a single point. Data from finance, partner companies, multimedia services, systems and applications must converge into a pattern in order to make informed business decisions.
Use of data for decision making
The uses of data for strategic decision making are wide-ranging – reporting, analytics, data mining, process mining, predictive and prescriptive analytics, performance metrics development, reporting, sharing with trusted partners, regulatory compliance, and more. These features can be used to find and develop new business opportunities. The data informing these functions should combine information from both the company’s proprietary internal sources and the market.
Internal data is often stored in structured systems. Unstructured and semi-structured data can be much more difficult to collect and process as it is stored in different locations by companies that do not have a common nomenclature. It is common for the image to contain far more unstructured or semi-structured data than structured data. Organizing this in a meaningful way is a good first step in organizational decision-making.
Understand types of data
Data from campaigns helps marketers identify patterns and learn more about the customer buying process: what appeals to the prospect, what helps them learn more about the company. Also, which regional and cultural preferences prospects prefer: a brief ad for learning or a more detailed document and much more. It’s about recognizing patterns, and the goal is to use those patterns to optimize business practices. This is about what makes our customers successful.
Data from marketing or advertising may include insights into customer and audience demographics, intent, behavior and more. Sales data should also be part of this equation for a complete view of the entire marketing funnel and purchase path. Stakeholders need to know the right metrics and Key Performance Indicators (KPIs) within it that can help inform future business strategy.
Data collection, analysis, and application for business decisions is complex, especially as the data is diverse (and often siled). That makes it challenging and interesting at the same time. This is also about pattern recognition.
Because enterprise data is diverse and often siled, it poses challenges for consolidation and analysis. The quality and accuracy of enterprise data are critical to its value and effectiveness. Records require attention and quality assurance before they are used.
Data analysis as a form of pattern recognition
Market analysis in itself is of great importance as it can help a company understand its competitors’ products and services and inform a company’s product development and marketing strategies.
So far we have talked about using customer data for analysis. Combine this with the insights we gather about competitors in the market and now the analysis starts to get stronger with additional context bringing together insights from the company and competitive companies in the market.
An additional point here is that it doesn’t have to be just competitors, this is about the ecosystem. Data collected from companies, competitors and the entire ecosystem leads us to this pattern recognition with common and different elements. This balance is necessary for making the right business decision, where you consider the relative information and not just the absolute data.
All data from all sources that is meaningful and relevant to the company’s goals must be integrated before it can be made usable. Data needs to be unified in a warehouse that stakeholders across the organization can access on-demand. Once unified, they must be cleaned to remove redundancies, structured, made compliant and private, run through quality assurance, sanitized and periodically re-evaluated to remove outdated or irrelevant data.
Why is big data analytics important?
Big data analytics enable stakeholders to uncover signals and trends that are relevant to business goals. It also allows modeling of unstructured or semi-structured data, including from social platforms, apps, emails or forms. Big data analytics handle data processing and modeling, as well as predictive analytics, visualization, AI (artificial intelligence), ad targeting, and other functions. It can also be used internally to optimize marketplace performance and customer relationships.
Big data analytics must be deployed with consideration for potential security issues and the overall quality of the data as new data is constantly being poured into the data warehouse.
Stakeholders should start with the overall focus area and goals. Then work towards collecting and analyzing data that adds up to the area of focus. As mentioned above, this helps in pattern recognition from multiple data sources, allowing insights to be gathered to select the right analytical tools and maintain quality control.
How companies use data
Businesses in every conceivable industry use big data, but one specific use case we can explore is gaming. Video games have strong user engagement, involve a social or communication aspect among players, and require significant technological investments to develop. Trading takes place within games – players can buy, trade or gain access to game features, bonuses and goods. Also, gambling is an incredibly competitive industry with countless gambling companies investing in advertising, marketing and development.
Gaming companies can use the data collected here to gain insights on how to promote and market their games, encourage players to pay for premium versions, deepen user engagement and conclusions for use in modeling or search to look for new business opportunities. You can also gain insights that can be used to customize experiences within the game for niche audiences or subgroups. It is possible to segment the existing data and create smaller audience segments relevant to the goals of each brand or product line. Many other industries use big data for the same reasons – consider how retailers use similar insights to recommend products to consumers.
How to qualify data
Qualifying data is a challenging process, but key to making stored data actionable. Qualifying data is a separate process from cleansing. It is the process of addressing ambiguities or generalizations in the data that must be qualified in order to specify what the data is intended to communicate for the benefit of the business. Qualification is also important to resolve discrepancies and inconsistencies in nomenclature that arise when data sets from different sources and companies are combined. The way a company qualifies data depends on its own goals, which need to be clarified before the qualification process.
Any conversation about collecting and processing data in 2022 needs to highlight the drastic changes that are underway in this area. Data providers that companies work with to supplement their own proprietary data must comply with GDPR (General Data Protection Regulation), CCPA, and other regulations that require user consent before their data is collected. Organizations need to understand how their external data partners manage compliance, identity, and personalization in this environment.
Many leading data providers are looking for contextual data to fill gaps they will see in the absence of rich data from third parties. In addition to providing insights into online and in-app consumer behavior, contextual data can also help make datasets more searchable as it can be used to analyze content consumers engage with and extract metadata from the digital environments where consumers spend time.
The applications and nuances of big data are varied and multiply and evolve over time. A company’s handling of Big Data must not be static. For reasons of competitiveness and compliance, every company should continuously reassess its stored data and the data management practices of all applicable business partners. An up-to-date, comprehensive data strategy is key to the progress of any modern business.
Gita Rao-Prasad is Senior Director of Growth Marketing at Agora.io
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