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How is industry adapting to the big data revolution?

How is industry adapting to the big data revolution?

Big data is booming, but every revolution comes with growing pains. What challenges do industries face regarding big data? How are these issues being addressed?

Data storage

“Clutter is the enemy of clarity.” – Julia Cameron

One serious data challenge is the existence of data silos, virtual storehouses of data that hold information that is not easily accessed by other groups within an organization. When data is siloed, it is being stored unhealthily. For big data to benefit an organization, various groups within the organization must be able to find the data easily, use it, and rely on it. 

Data that is stored across silos within an organization is far more likely to be inconsistent and inaccurate. If an organization simply crams its data into silos without breaking it down in meaningful ways, it will be impossible to get a big-picture view of company data. This will hinder any benefits a company could hope to glean from a digital transformation. Siloed data is difficult to govern as it invites regulatory compliance problems and misuse of sensitive data. 

Effective data analysis informs business decisions and protects an organization from threats. The ETL process (extract, transform, and load) automates the transfer of data from different sources to an integrated data warehouse. Cloud-based ETL tools utilize cloud infrastructure to address data integrity concerns to ensure that data is continually updated. Cloud computing stocks lead these moves to maximize use of data through means such as breaking down data silos. 

Responsible data management

“Experts often possess more data than judgement.” – Colin Powell

How can organizations make the right decisions regarding data? Many organizations have taken to hiring a company data steward. Data stewards provide the governance a company needs to better manage data. They uphold data quality and ensure the company is leveraging its data assets as optimally as possible. As data continues to increase in volume and variety, the role of data stewards becomes more crucial than ever. 

Data stewards oversee all data-related issues in an organization. In some companies, this need for high-level oversight is carried out by a Chief Data Officer, or CDO. A CDO takes centralized ownership of maintaining company data, with an eye towards innovation, management, and adjustment of data practices as needed. A CDO understands the big picture of how data is being collected, stored, and used within the organization. This understanding allows them to quickly identify the root cause of any problems in the data pipeline. 

The CDO should lead a data unit, which includes data engineers, data analysts, data scientists, and other data stewards who ensure that the company’s process for handling data is consistent with its vision and mission. The data unit can also educate other employees in the organization so that they are not intimidated or overwhelmed by data tools nor by the information influx.  

Intelligent Data Strategizatio

“Work business-backwards, not data-forward.” – McKinsey report 1

Data provides value to an organization. Some companies get bogged down by amassing data without applying the data. Data strategization has the potential to drive company decisions and yield growth opportunities. 

Companies must first do the necessary background work to identify what meaning they hope to extract from data. First, an organization should hone in on what problem they want to solve with data. Companies can get lost in the vast amounts of data they collect without knowing how to achieve their goals. A clear set of business objectives allow a company to define what they want their data to inform. 

An organization’s leaders must be aligned with one another in terms of the top strategic priorities of the company. Data unit members must collaborate to determine which aspects of the business need to be improved through data and to strategize how they should go about doing so. 

Addressing the data skills shortage

The overwhelming need for more data scientists and skilled analysts continues to grow, and the demand exceeds the supply. This skills shortage directly impedes big data implementation.2 Organizations have been quick to incorporate big data into their strategies, but without sufficient skilled data specialists, they will be unable to take full advantage of the benefits that big data has to offer. 

How can the skills shortage be addressed quickly and affordably? Companies must utilize their data stewards to educate employees within the organization. Data must be made accessible and usable so that even employees without extensive knowledge of coding or algorithms can become more data literate. Those employees who are already technically proficient should be offered skills training, workshops, and training to learn data skills such as modeling, machine learning, data architecture, and data engineering. 

Big data can be simplified to make it more user-friendly. Self-service tools like data dashboards and analytics reporting platforms serve to make data meaningful to regular employees. Actionable insights should be drawn from simple visualizations that can create meaning from big data. 

Data Agility

“Don’t wait for perfection before you start. Start somewhere so you can have something tangible you can work to perfect.” – Simon Sinek

Across different industries, the business ecosystem evolves more rapidly than ever, demanding agility. Too easily, data becomes outdated, losing its usefulness. The issue of stale data reminds us that analytics must become more agile. For instance, consumer behavioral patterns and preferences have been undoubtedly altered in light of the COVID-19 pandemic. Many previously-held business assumptions are completely invalid, and the new reality often has yet to provide meaningful current data to replace those assumptions. 

Companies can try to keep up with their data by breaking it down into workable chunks. Simple, low-tech automations can gradually be worked up to large-scale implementations. An agile approach to data implementation involves quicker kickoff times and continual openness to be flexible throughout the development cycle. Testing is done throughout the process, allowing for necessary adjustments to data models as needed.

Agility in big data management prevents data from stagnating. It allows projects to move forward rapidly even before every bit of data has been dealt with. The data team works to test hypotheses and make adjustments on a continuous basis with fresh data. This significantly shortens workflows, lessens the impact of data drift, and ensures that models make accurate predictions.  

Fortifying data security

“Safety isn’t expensive, it’s priceless.” – Jerry Smith

Big data provides enormous opportunities for business growth, and data security must be thoroughly implemented to protect it from cyberattacks or corruption. Much the way someone would never buy a large home without installing a security system and locking the windows and doors, a security strategy is an absolute must. Data security protects company assets and maintains an organization’s reputation. Malicious attackers are all too eager to steal customer information, and breaches cost time and money to deal with. 

Having a successful security policy means that a company must know its data, know where it is stored, and understand how it is used. Cloud security needs include tracking data movement, ranking data by level of risk, and protecting and monitoring end-points. 

A company’s big data security must suit its business needs and protect end-users. For example, data encryption can help companies maintain the security of both physical and cloud data. Tokenization is used when particularly sensitive data is stored, such as credit card information or social security numbers. It replaces data with unique identifiers that allow for data to be condensed and secured. Authentication protects sensitive information such as user names and passwords. Users must authenticate or prove their identity through means such as fingerprints, voice recognition, or retina scans.

Finally, organizations must expect the unexpected and put backups in place. Backing up data ensures accessibility in the event of a cyber attack. Storage backups should include databases, files, systems, configurations, and applications, as well as data stored on mobile devices and tablets. 

To sum up, companies must employ multi-faceted approaches to handling big data lest the opportunities it offers be hindered by the potential pitfalls. Big data stocks and Big data ETFs tap the forefront of the progression of industries towards big data implementation, ensuring that companies evolve robustly, rapidly, and responsibly. By diversifying one’s portfolio with a big data ETF, investors can take part in securing big data’s immense potential.

1 https://www.mckinsey.com/~/media/mckinsey/business%20functions/mckinsey%20analytics/our%20insights/achieving%20business%20impact%20with%20data/achieving-business-impact-with-data_final.ashx#:~:text=%E2%80%9CThink%20business%20backwards%2C%20not%20data%20forward.%E2%80%9D&text=Operating%20model%20is%20the%20underlying,the%20insights%20value%20chain%20lives.

2 https://itrexgroup.com/blog/5-big-data-challenges/#