Business leaders want to ensure their data operations align with their brand values and corporate mission statements. Therefore, they seek reliable data acquisition, transformation, analysis, and report management roadmaps. Several strategic approaches can help them accomplish this vision. This post will explain how data strategy drives decision-making to maximize business relevance in data operations.
What is a Data Strategy?
A data strategy is a systematic resource allocation approach that guides managers, IT teams, and an organization’s departments on data gathering, insight exploration, reporting, and governance. It also becomes a knowledge repository for experienced and newly-recruited professionals at the company. As a result, modern enterprises employ data strategy consulting services to enhance their data quality and extract-transform-load (ETL) pipelines.
When a company determines a data strategy, team leaders get a valuable tool to increase coordination between team members. Besides, strategizing reminds all the stakeholders to avoid deviation from the core objectives. These advantages prove that a data strategy is indispensable to harnessing the power of enterprise data for decision-making.
Why Do Businesses Require a Custom Data Strategy?
Every organization will require a unique data strategy to deliver its products and services, personalize marketing efforts, and mitigate business development risks. Therefore, a standard business strategy is insufficient, and expert consultation with a background in your target industry is the need of the hour.
Consider a disaster management or fraud detection firm. These companies will tremendously benefit from technologies like predictive analytics solutions to enhance their performance. However, a disaster manager’s data needs differ from what a fraud investigator would want to analyze.
Estimating disaster risks will be based on geological, environmental, season-specific, and infrastructure-related intelligence. Meanwhile, financial transactions, credit risks, technical data, and an individual’s behavioral insights will matter more to fraud analysts.
If one of these firms serves global clients, its data governance compliance must evolve according to each geopolitical territory’s local regulations. These cases help demonstrate how data requirements vary wildly across all industries and operational scales. So, corporations must often seek a custom data strategy adequate for their particular use cases.
The Essential Components of a Data Strategy for Decision-Making
All data strategies are distinct due to what a business wants to do with data. However, most strategies include the components below to ensure a robust, outcome-oriented data processing workflow.
1| Data Source and Method Selection
Data quality influences the reliability of insight extraction, reporting, and decision-making. Therefore, brands must practice caution throughout the initial data acquisition stages. A company with top-notch data processing tools can still get skewed insights if data sources provide poor-quality intelligence.
When selecting a data source, the strategists must evaluate the domain expertise of the data provider. For example, consider how old the data source is and whether it has a reputation as an authoritative data provider.
Remember, social media content can mislead a brand about a consumer’s living standards. It might be essential to anticipate how customers appreciate or criticize your offerings. However, users often magnify their genuine opinions or emotions about your brand for fame or virality.
Comparatively, face-to-face interviews or market research surveys provide better responses with less risk of lying, exaggeration, or impulse-led rating. As such, a comprehensive data strategy clarifies how to decide on a data source and data-gathering mechanism for robust decision-making.
2| Business Relevance Assessment for Data Quality
Social listening analysts will use automated data acquisition programs to collect unstructured data on your competitors and consumers. Likewise, tracking brand mentions across news publications, industry magazines, and professional case studies will help you estimate your brand’s reach. And today’s big data tech has also become more effective in interpreting sentiments and context.
Nevertheless, an extensive dataset can be both: a blessing and a curse. After all, your IT ecosystem will have a more significant burden on the data storage components. Therefore, the following activities in a data strategy are vital for efficient data management, relevant insight discovery, and profitable decision-making.
- Compress the data your company genuinely needs to serve stakeholders’ business queries.
- Also, archive the historical performance intelligence that gets occasional reviews by auditors, quality inspectors, executives, or new board directors.
- Delete the “noisy” data that consumes digital storage and bandwidth without exhibiting business model relevance.
If a brand develops a holistic data strategy, it can assign appropriate managers and analysts to perform the above functions. Otherwise, the irrelevant excess intelligence will affect analytics and reporting efficiency.
3| Cybersecurity Measures for Data Protection
Birthdays, pet names, favorite songs, and best friend names are unsuitable as passwords. While one might think it is a well-known principle, the reality will demonstrate otherwise. After all, most data leaks and malware injection result after cybercriminals win with brutal force attacks.
The simpler the password, the easier to crack it using iterative methods. Besides educating the company’s stakeholders on creating solid passwords, the IT and data strategy teams must find unencrypted digital assets. End-to-end encryption (E2EE) and cloud-integrated antimalware applications are integral to reducing data theft or ransomware. Additionally, a professionally-crafted data strategy will compel brands to invest in cutting-edge firewalls and virtual private networks (VPNs).
Cybersecurity considerations extend to hardware repairs and upgrades. They may comprise integrity checks for surveillance cameras, always-on gadgets, Wi-Fi routers, authentication cards, data center servers, employee workstations, and biometric sensors. Remember, your organization’s data will continuously move between these assets. So, protecting them is critical.
4| Data Processing Risk Analysis
Nations in Europe, the Americas, Asia, Africa, and Australia have directed all enterprises to treat consumer, employee, and investor data according to regional requirements. Furthermore, non-compliance can affect a brand’s trade liberties, financial performance, and reputation among stakeholders. Therefore, global and local businesses must mitigate varying privacy and data protection risks.
Alongside these legal risks, data processing makes your company vulnerable to data leak controversies. Since no IT ecosystem is 100% invincible against experienced hackers, a single leak can have an extensive scope. Imagine what you can do if a data leak involves exposing personally identifiable information (PII) of your entire consumer base.
Your data operations are prone to human risks. For instance, employees might open unsolicited emails on a work computer containing ransomware. A repaired machine can receive hardware-level malicious codes. Similarly, a manager might leave the laptop having confidential intelligence unattended for a quick snack break.
Data strategists must brainstorm ideas and policies to mitigate the risks discussed above. Their knowledge of human behavior, macroeconomic risks, and corporate practices must be vast enough to deliver a resilient data strategy.
5| Data Governance for Accountability and Transparency
While data democratization is at the core of corporate digital transformation, embracing data governance is non-negotiable. Corporate espionage is a financially material threat. After all, no company wants its trade secrets stolen by competitors.
As a result, brands must adopt an adequate user authentication and document approval system. This system must balance the data availability needs in a data strategy with confidentiality requirements. Let the qualified and authorized workers enjoy flexible data access, but prevent potential misuse of this privilege.
Reputed data strategy providers will empower organizations to benefit from usage and access logs. Simultaneously, creating traceable digital signatures and entry-exit passes is recommendable.
Still, the data strategy must guide business leaders on financial performance disclosure to comply with sustainability accounting and investor relations norms. Stringent data governance does not mean absolute secrecy. It indicates that the brand recognizes it needs a reasonable trade-off between transparency and intelligence protection.
Conclusion
Data strategists help corporations streamline and align their data operations according to founders’ and investors’ requirements. They also explore how a client’s financial, legal, and functional risk exposure fluctuates due to data collection and processing.
Since managers require relevant insights, data strategy is vital across their decision-making routines. Irrespective of what might seem true due to intuition or empirical evidence, appropriate data-led policies are more likely to help companies overcome growth challenges. And a data strategy ensures data quality and governance to harness the full power of this approach.
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