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BlockDrive can assist legal services firms to identify data quality issues either in their own business applications or from clients. Our objective is to limit undue risks and reputational damages from data inaccuracies.
BlockDrive has led advanced initiatives to identify fraud affecting the IRS, Homeland Security & USDA. Dirty data negatively impacts policies, decisions, lowers trust, creates liabilities and civil liberties concerns, increases costs, and can in some cases produce serious harm or even loss of life.
Utilizing BlockDrives data intelligence to identify bad data in supply chain systems, users avoid ordering based on inaccurate data avoiding excess inventory, shortages, and headaches that deeply impact the bottom line.
BlockDrive helps identifying bad data in food industries which is a prerequisite to enable process reliability and improved customer loyalty. Poor data quality contributes to wasted resources and time.
According to a study published by retailers, retail businesses lose, on average, $9.7 million each year due to poor quality data, and workers waste 50% of their time hunting for data, correcting errors and confirming sources for uncertain data. Inaccurate data will cripple marketing campaigns, decrease customer satisfaction and trust, cause an inability to make informed decisions and cost the company missed opportunities. Utilizing the power of BlockDrive AI these issues can be solved.
Low quality or bad data impact energy industries either in the actual power production of assets, or power prices on the open market. It also impacts renewable energy industries especially on the energy consumption or production decisions. This costs energy companies more in the long-term. By empowering your organization with BlockDrive you avoid unnecessary loss of trust.
Poor quality data leads to poor patient care, negatively affecting the validity and reproducibility of research results and limiting the value that such data for public health surveillance. Bad data will also impact healthcare payers with the wrong matrix of health plans ultimately affecting the bottom line.
Bad data can have a lasting adverse impact, as insurers are affected in many different ways, including loss of business opportunities through inaccurate pricing, loss of customer loyalty by poor policy in-force management and a poor claims experience, higher fraud losses and lower fraud detection and lower competitiveness leading to losses of revenue and profits.
Travel & Hospitality
Travel related data is often inaccurate due to the nature of the data being non-standard and fluid. There is often no single source of truth. The bad data will impact the analytic reports, decisions of travel programs and bottom lines.
BlockDrive can help to identify bad data for real estate, which can seriously erode confidence, impact decisions and projections. Poor quality data increases operational risk and create siloed processes
Charity and non-profit organizations rely on data to setup their campaigns and fund raising audience. BlockDrive can identify bad data which can influence campaign timing, audience and ultimately, cost the organization in millions if bad decisions are made.
Branding is especially important for luxury goods. BlockDrive will identify bad data which leads to wrong branding effort and decisions. A particular interesting example of bad data used by Luxury brands is they failed to successfully target millennials in China by underestimating their purchasing power, which resulted in loss profit for these Luxury brands.
According to Gartner research, the financial cost of poor data quality is $15 million per banking firm back in 2017. Bad data costs exponentially more for financial firms today due to loss of productivity, efficiency and accuracy. BlockDrive can continuously monitor bad data in transactions or batches, therefore, gives organizations a head start for data improvement.
BlockDrive can identify poor quality data and bad data therefore allowing manufacturers to implement better data solutions that fit into the business strategy and promote accountability by assigning people to drive that strategy. In contrast, bad data can easily lead to costly inefficiencies.
With the trend of smart driving, autonomous and electrical vehicles on the rise, automakers are rapidly becoming data companies. As an example, each driver will create an estimated 1.7 MB of data per second. BlockDrive helps to identify bad data which leads to costly recalls and repairs, and allow the automakers to create accurate reporting & analytic solutions.
The clothing industry produces and sells somewhere between 80 billion and 150 billion garments a year globally. BlockDrive helps to eliminate bad data so clothing manufacturers can create better designs, better marketing strategies and eliminate unnecessary waste & greenhouse gas emission.
BlockDrive use for educational institutions can lead to improved data quality, as bad data results in bad policy decisions. In the private education sector, bad data will lead to inaccurate marketing strategies and loss of profit.