As data science evolves, key challenges are driving organizations to seek innovative solutions to compete in the new -driven economy.
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In late 2018, a survey by Univa found that 96% of respondents expected an “explosion in projects” in production by 2020. International Data Corp. forecasts that spending on
The following five factors are causing these slowdowns, each with its own set of challenges and opportunities:
1. Making data actionable for data science
The same Dimensional Research article found that 96% of respondents cited data quality and data labeling as crucial problems slowing their
Businesses store vast amounts of data, but often in different lines of business, across disparate systems and with varying levels of leadership and governance. Whether through manual processes or by leveraging automation, the first struggle for data science teams is to access and collect relevant data from different sources. Chief information officers and chief data officers must lead the charge to make data actionable for data science. Mitigating challenges related to data integration, ETL , security, and data privacy will drive faster turnaround of data science projects and making data science quicker and more efficient.
2. Shortage of data science talent
A 2018 LinkedIn survey found a lack of over 150,000 people in the U.S. with data science skills. The rapid adoption of
3. Time-to-value must accelerate
The plodding pace of development also slows data science. Data science projects are iterative in nature due to the uncertainty of data and require a deep understanding of underlying business problems. Data scientists create a series of hypotheses to be tested and validated with actual business data by wrangling, cleansing, joining, combining, and aggregating data to identify data relationships and extract relevant patterns to build
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