You're close, but not quite there yet.
Using Bayes' Theorem we know that: $$P(H_{1}|Data) = \frac{P(Data|H_{1}) P(H_1)}{P(Data)}$$.
However we cannot state that: $$P(Data) \neq \sum_{i}^{4} P(Data|H_{i})$$, from definition we know that $P(Data) = \sum_{i}^{4}P(Data \cap H_{i})$, which is equivalent to saying that $$P(Data) = \sum_{i}^{4}P(Data|H_{i})*P(H_{i})$$.
So in order to fix your solution you would only have to scale each of the terms in the denominator with the corresponding probability of $P(H_{i})$.
So to summarize using your original notation we can compute $P(H_{1}|Data) = \frac{P(Data|H_{1}) P(H_1)}{P(Data|H_{1})*P(H_{1}) + P(Data|H_{2})*P(H_{2}) + P(Data|H_{3})*P(H_{3}) + P(Data|H_{4})*P(H_{4})}$