Benefits and Risks of Big Data

Benefits of Big Data

1. Cost reduction: Big data can reduce costs in storing all business data in one place. Tracking analytics also helps companies find ways to work more efficiently to cut costs wherever possible.

2. Strategic business decisions: The ability to constantly analyse data helps businesses make better and faster decisions, such as cost and supply chain optimization.

3. Product development: Developing and marketing new products, services, or brands is much easier when based on data collected from customers’ needs and wants. Big data analytics also helps businesses understand product viability and to keep up with trends.

4. Risk management: Businesses can identify risks by analyzing data patterns and developing solutions for managing those risks.

5. Customer experience: Data-driven algorithms help marketing efforts (targeted ads, for example) and increase customer satisfaction by delivering an enhanced customer experience.

Risks of Big Data

1. Data Security: This risk is obvious and often uppermost in our minds when we considering the logistics of data collection and analysis. Data theft is a rampant and growing area of crime and attacks are getting bigger and more damaging. In fact five of six most damaging data theft of all time were carried out within the last two years.

2. Data Privacy: Closely related to the issue of security is privacy. But in addition to ensuring that people’s personal data are safe from criminals, you need to be sure that the sensitive information you are storing and collecting isn’t going to be divulged through less malevolent but equally damaging misuse by yourself.

3. Costs: Data collection, aggregation, storage, analyze and reporting all cost money. On top of this, there will be compliance costs – to avoid falling foul on the issues I raised in the previous point. These costs can be mitigated by careful budgeting during the planing stages. But getting it wrong at that point can lead to spiraling costs, potentially negating any value added to your bottom line by your data-driven initiative. This is why “starting with strategy” is so viral.

4. Speed of data growth: Big data, by nature, is always rapidly changing and increasing. Without a solid infrastructure in place that can handle your processing, storage, network, and security needs, it can become extremely difficult to manage.

5. Problems with data quality: Data quality directly impacts the quality of decision-making, data analytics, and planning strategies. Raw data is messy and can be difficult to curate. Having big data doesn’t guarantee results unless the data is accurate, relevant, and properly organized for analysis. This can slow down reporting, but if not addressed, you can end up with misleading results and worthless insights.

6. Compliance violations: Big data contains a lot of sensitive data and information, making it a tricky task to continuously ensure data processing and storage meet data privacy and regulatory requirements, such as data localization and data residency laws.

7. Integration complexity: Most companies work with data siloed across various systems and applications across the organization. Integrating disparate data sources and making data accessible for business users is complex, but vital, if you hope to realize any value from your big data.