It’s been more than a decade, but big data remains an overwhelming challenge for most organisations. According to a survey from Capgemini Research Institute, only half of the companies say that data drives their decision-making. Forty-three per cent say that they have been able to monetise their data through products and services.
What can be the big data roadblocks holding organisations from extracting impactful insights from tons of information they have been collecting?
Poor Data Quality
The problem with any data in an organisation is that it is stored in multiple locations and formats. Having a look at production costs might seem impossible for a manager when finance keeps track of supplies costs, payroll, and other financial data, as it should while manufacturing machines’ data is sitting unintegrated in the production department’s database as it shouldn’t.
With big data, the silo challenge looms larger. The amount of data, various data governance strategies, internal and external sources, and additional security and privacy requirement systems also make data consolidation difficult or impossible.
A significant challenge with big data is that it is never 100 per cent consistent. It can also be quite challenging to get a complete picture of shipments going to Dubai, say if the sales team handles local clients under the Dubai tag. At the same time, the production uses the DXB acronym, while finance uses an entirely different city code. The various levels of granularity employed to manage the databases only further aggravate the big data analytics problem.
Data is prone to errors as well. If you have more datasets, you are more likely to get the same data misrepresented with different errors and margins of error. Duplicate records can also complicate your big data analytics.
If you want workable data, you must build a data governance framework. The framework establishes policies, procedures, and processes to ensure the quality of your data, make it visible, and install security safeguards. It’s essential to align your data governance with business needs. For instance, if you are in healthcare, it definitely should be centred around compliance with HIPAA or other industry standards.
Implementing master data and metadata management practices will allow you to address big data’s quality and consistency challenges. Consolidating master data (your crucial business information about customers, products, suppliers, or locations) is a wise choice. Using this model, master data is merged from different sources into a central repository that serves as a “golden record” or a single version of the truth, eliminating the duplication and redundancy problems associated with big data.
To manage metadata, you will need a data catalogue. Data discovery is essentially an inventory of all your data assets. In addition to business glossaries, advanced data catalogues carry out data quality checks, provide lineage, and assist with data preparation. To ensure that data entries match catalogue definitions, however, hard-and-fast validation rules are needed. Both business and IT people should participate in defining them.
Consider quality when setting up applications as part of managing your entire IT ecosystem, but define data requirements based on your use cases. First, identify your business problem or use case and determine the data you need to solve it. Only then should requirements for data be carefully considered. Next, organise your data into logical layers. The idea is to integrate, treat, and transform your data step by step to reach the analytics layer is a high-quality resource that business users can use.
Utilise technology innovations to automate and improve parsing, cleaning, profiling, data enrichment, and other data management processes. There are many good data management tools on the market.
The role of data stewards is crucial. The governance of data is not only about standards and technologies but also about people. The data stewards are responsible for data quality and serve as a central point of contact for all data-related issues. Their deep understanding of data lineage (how data is captured, changed, stored, and utilised) enables them to identify the root cause of problems in data pipelines.
Lack of Coordination To Steer Big Data
Since data analytics lacks a central point of accountability, they usually end up being poorly focused. Ad hoc projects implemented by a business or IT team lead to missteps and uninformed decisions. No matter how brilliant, any data governance strategy is doomed if there’s no one to coordinate it. Moreover, a disjointed approach to data management makes it impossible to understand what data is available at the organisational level, let alone prioritise its use.
With no visibility into its data assets, the company gets incorrect answers from algorithms fed junk data and faces increased privacy and security risks. Also, data teams process data without any business value, with no one taking ownership.
Every data-driven organisation needs a centralised role like the chief data officer (CDO) responsible for defining rules as part of data governance and ensuring that these are followed for all data projects. All IT initiatives are related to data in some way, whether you want to spin off a database, build a new application, or update an old system. The CDO is instrumental in setting the company’s strategic data vision, driving data governance policies, and adjusting processes to the mastery of the organisation.
Creating data tribes or centres of excellence is also a good idea. These teams are typically composed of data stewards, data engineers, and data analysts who work together to build the company’s data architecture. Additionally, they will assist in addressing big data’s coordination issues. You must measure your data tribe’s performance by the number of big data use cases they identify and successfully implement. Therefore, they will be motivated to help other teams maximise their new technologies and data potential.
Another crucial mission of data squads is education. It engages people, teaches them how to use new tools and work use cases, and most importantly, helps them change their daily routines.
Shortage of Skills
This problem is relatively straightforward in big data implementation: demand for data science and analytics skills has outpaced supply.
According to Glassdoor’s 50 Best Jobs in America report for 2020, data scientists remain among the top three jobs. According to a QuantHub survey, there will be a shortage of 250,000 data science professionals in 2020. Thirty-five per cent of respondents said they expected to have the most challenging time attracting data science skills, which were second only to cybersecurity.
The reason being organisations have rushed to adopt big data analytics to lay hands on data-powered revenue sources and not lose competitors’ opportunities. With the skills shortage, they, however, are having difficulty taking advantage of their data.
Grow your tech talent to solve this big data challenge. Reskill and upskill your technical employee base, but focus on upskilling. People dislike change, so adding a new skill — data modelling, data architecture, data engineering, or machine learning — to their already appropriate skillset is a good start.
Run training programs and workshops for your tech staff, but make sure the time and resources are not wasted. Ask them to contribute something valuable as a follow-up. It could be an improvement to their workflow or another business process. Develop partnerships with higher education institutions to discover promising junior talent.
If you don’t have the in-house data skills or need a niche solution for implementing big data, seek a strategic partner. Identify your business problem first, and only then search for a highly-skilled tech partner that has solved a similar business problem in the past.
Democratise your data radically to make it accessible and usable for employees with no specialised algorithm or coding knowledge. Educating all employees on data topics will help you tackle the big data skills shortage problem by improving data literacy and promoting data adoption. Data stewards should also participate actively in the initiative.
Simplify analysis for business users with self-service analytics tools, such as dashboards and recommendation systems. Encourage them to use data-driven decisions daily. A good example would be a global digital industrial conglomerate that has developed an analytics platform that incorporates a semantic business layer to give employees access to the data they work with every day, from HR and finance to marketing and production.
If you don’t know what to do with your data, it will not produce insights no matter how adept your technical team is. Regular front-line employees should do analytics, create simple visualisations, and tell stories, translating data into explicit action.